@article{Kokkotis2020,
abstract = {Knee Osteoarthritis (KOA) is a multifactorial disease that causes low quality of life, poor psychology and resignation from life. Furthermore, KOA is a big data problem in terms of data complexity, heterogeneity and size as it has been commonly considered in the literature with most of the reported studies being limited in the amount of information they can adequately process. The aim of this paper is: (i) To provide a robust feature selection (FS) approach that could identify important risk factors which contribute to the prediction of KOA and (ii) to develop machine learning (ML) prediction models for KOA. The current study considers multidisciplinary data from the osteoarthritis initiative (OAI) database, the available features of which come from heterogeneous sources such as questionnaire data, physical activity indexes, self-reported data about joint symptoms, disability and function as well as general health and physical exams' data. The novelty of the proposed FS methodology lies on the combination of different well-known approaches including filter, wrapper and embedded techniques, whereas feature ranking is decided on the basis of a majority vote scheme to avoid bias. The validation of the selected factors was performed in data subgroups employing seven well-known classifiers in five different approaches. A 74.07% classification accuracy was achieved by SVM on the group of the first fifty-five selected risk factors. The effectiveness of the proposed approach was evaluated in a comparative analysis with respect to classification errors and confusion matrices to confirm its clinical relevance. The results are the basis for the development of reliable tools for the prediction of KOA progression.},
author = {Kokkotis, Christos and Moustakidis, Serafeim and Giakas, Giannis and Tsaopoulos, Dimitrios},
doi = {10.3390/app10196797},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Kokkotis et al. - 2020 - Identification of risk factors and machine learning-based prediction models for knee osteoarthritis patients.pdf:pdf},
issn = {20763417},
journal = {Applied Sciences (Switzerland)},
keywords = {Clinical data,Feature selection,KL-grade,Knee osteoarthritis,Machine learning,Prediction},
number = {19},
title = {{Identification of risk factors and machine learning-based prediction models for knee osteoarthritis patients}},
volume = {10},
year = {2020}
}
@article{Hunter2019,
author = {Hunter, David J and Bierma zeinstra, Sita},
doi = {10.1016/S0140-6736(19)30417-9},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Hunter, Bierma-zeinstra - 2019 - Seminar Osteoarthritis.pdf:pdf},
title = {{Seminar Osteoarthritis}},
volume = {393},
year = {2019}
}
@article{Eckstein2014,
abstract = {The Osteoarthritis Initiative (OAI) is a multicentre, prospective, observational, cohort study of knee osteoarthritis (OA) that began recruitment in 2004. The OAI provides public access to clinical and image data, enabling researchers to examine risk factors/predictors and the natural history of knee OA incidence and progression, and the qualification of imaging and other biomarkers. In this narrative review, we report imaging findings and lessons learned 10 years after enrolment has started. A literature search for full text articles published from the OAI was performed up to 31 December 2013 using Pubmed and the OAI web page.We summarise the rationale, design and imaging protocol of the OAI, and the history of OAI publications. We review studies from early partial, and later full OAI public data releases. The latter are structured by imaging method and tissue, reviewing radiography and then MRI findings on cartilage morphology, cartilage lesions and composition (T2), bone, meniscus, muscle and adipose tissue. Finally, analyses directly comparing findings from MRI and radiography are summarised. Ten years after the first participants were enrolled and first papers published, the OAI has become an invaluable resource to the OA research community. It has fuelled novel methodological approaches of analysing images, and has provided a wealth of information on OA pathophysiology. Continued collection and public release of long-term observations will help imaging measures to gain scientific and regulatory acceptance as 'prognostic' or 'efficacy of intervention' biomarkers, potentially enabling shorter and more efficient clinical trials that can test structure-modifying therapeutic interventions (NCT00080171).},
author = {Eckstein, Felix and Kwoh, C Kent and Link, Thomas M},
doi = {10.1136/annrheumdis-2014-205310},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Eckstein, Kwoh, Link - 2014 - Imaging research results from the Osteoarthritis Initiative (OAI) a review and lessons learned 10 years af.pdf:pdf},
isbn = {2014205310},
issn = {1468-2060},
journal = {Annals of the rheumatic diseases},
number = {Figure 3},
pages = {1289--1300},
pmid = {24728332},
title = {{Imaging research results from the Osteoarthritis Initiative (OAI): a review and lessons learned 10 years after start of enrolment.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24728332},
volume = {2006},
year = {2014}
}
@article{Thysen2015,
abstract = {Osteoarthritis is a chronic degenerative disorder of the joint and represents one of the most common diseases worldwide. Its prevalence and severity are increasing owing to aging of the population, but treatment options remain largely limited to painkillers and anti-inflammatory drugs, which only provide symptomatic relief. In the late stages of the disease, surgical interventions are often necessary to partially restore joint function. Although the focus of osteoarthritis research has been originally on the articular cartilage, novel findings are now pointing to osteoarthritis as a disease of the whole joint, in which failure of different joint components can occur. In this Review, we summarize recent progress in the field, including data from novel 'omics' technologies and from a number of preclinical and clinical trials. We describe different in vitro and in vivo systems that can be used to study molecules, pathways and cells that are involved in osteoarthritis. We illustrate that a comprehensive and multisystem approach is necessary to understand the complexity and heterogeneity of the disease and to better guide the development of novel therapeutic strategies for osteoarthritis.},
author = {Thysen, S. and Luyten, F. P. and Lories, R. J. U.},
doi = {10.1242/dmm.016881},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Thysen, Luyten, Lories - 2015 - Targets, models and challenges in osteoarthritis research.pdf:pdf},
isbn = {1754-8403\r1754-8411},
issn = {1754-8403},
journal = {Disease Models \& Mechanisms},
keywords = {animal models,bone,cartilage,osteoarthritis},
number = {1},
pages = {17--30},
pmid = {25561745},
title = {{Targets, models and challenges in osteoarthritis research}},
url = {http://dmm.biologists.org/cgi/doi/10.1242/dmm.016881},
volume = {8},
year = {2015}
}
@article{kerkhof2013a,
annote = {PubMed PMID: 23962456.},
author = {Kerkhof, H J and Bierma-Zeinstra, S M and Arden, N K and Metrustry, S and Castano-Betancourt, M and Hart, D J and Hofman, A and Rivadeneira, F and Oei, E H and Spector, T D and Uitterlinden, A G and Janssens, A C and Valdes, A M and Meurs, J B},
doi = {10.1136/annrheumdis-2013-203620.},
journal = {Ann Rheum Dis},
pmid = {23962456},
title = {{Prediction model for knee osteoarthritis incidence, including clinical, genetic and biochemical risk factors}},
volume = {2014;73(12}
}
@article{Eckstein2016,
abstract = {? 2015, European Society of Radiology.Objective: To evaluate whether change in fixed-location measures of radiographic joint space width (JSW) and cartilage thickness by MRI predict knee replacement. Methods: Knees replaced between 36 and 60?months? follow-up in the Osteoarthritis Initiative were each matched with one control by age, sex and radiographic status. Radiographic JSW was determined from fixed flexion radiographs and subregional femorotibial cartilage thickness from 3 T MRI. Changes between the annual visit before replacement (T0) and 2?years before T0 (T-2) were compared using conditional logistic regression. Results: One hundred and nineteen knees from 102 participants (55.5?% women; age 64.2 ? 8.7 [mean ? SD] years) were studied. Fixed-location JSW change at 22.5?% from medial to lateral differed more between replaced and control knees (case-control [cc] OR = 1.57; 95?% CI: 1.23?2.01) than minimum medial JSW change (ccOR = 1.38; 95?% CI: 1.11?1.71). Medial femorotibial cartilage loss displayed discrimination similar to minimum JSW, and central tibial cartilage loss similar to fixed-location JSW. Location-independent thinning and thickening scores were elevated prior to knee replacement. Conclusions: Discrimination of structural progression between knee pre-placement cases versus controls was stronger for fixed-location than minimum radiographic JSW. MRI displayed similar discrimination to radiography and suggested greater simultaneous cartilage thickening and loss prior to knee replacement. Key Points: ? Fixed-location JSW predicts surgical knee replacement more strongly than minimum JSW. ? MRI predicts knee replacement with similar accuracy to radiographic JSW. ? MRI reveals greater cartilage thinning and thickening prior to knee replacement.},
author = {Eckstein, Felix and Boudreau, Robert and Wang, Zhijie and Hannon, Michael J. and Duryea, Jeff and Wirth, Wolfgang and Cotofana, Sebastian and Guermazi, Ali and Roemer, Frank and Nevitt, Michael and John, Markus R. and Ladel, Christoph and Sharma, Leena and Hunter, David J. and Kwoh, C. Kent},
doi = {10.1007/s00330-015-3977-8},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Eckstein et al. - 2016 - Comparison of radiographic joint space width and magnetic resonance imaging for prediction of knee replacement.pdf:pdf},
issn = {14321084},
journal = {European Radiology},
keywords = {Clinical validation,Knee osteoarthritis,Magnetic resonance imaging,Measurement performance,Radiographic joint space width (JSW)},
number = {6},
pages = {1942--1951},
publisher = {European Radiology},
title = {{Comparison of radiographic joint space width and magnetic resonance imaging for prediction of knee replacement: A longitudinal case-control study from the Osteoarthritis Initiative}},
url = {http://dx.doi.org/10.1007/s00330-015-3977-8},
volume = {26},
year = {2016}
}
@article{Peterfy2008,
abstract = {Objectives: To report on the process and criteria for selecting acquisition protocols to include in the osteoarthritis initiative (OAI) magnetic resonance imaging (MRI) study protocol for the knee. Methods: Candidate knee MR acquisition protocols identified from the literature were first optimized at 3 Tesla (T). Twelve knees from 10 subjects were scanned one time with each of 16 acquisitions considered most likely to achieve the study goals and having the best optimization results. The resultant images and multi-planar reformats were evaluated for artifacts and structural discrimination of articular cartilage at the cartilage-fluid, cartilage-fat, cartilage-capsule, cartilage-meniscus and cartilage-cartilage interfaces. Results: The five acquisitions comprising the final OAI MRI protocol were assembled based on the study goals for the imaging protocol, the image evaluation results and the need to image both knees within a 75 min time slot, including positioning. For quantitative cartilage morphometry, fat-suppressed, 3D dual-echo in steady state (DESS) acquisitions appear to provide the best universal cartilage discrimination. Conclusions: The OAI knee MRI protocol provides imaging data on multiple articular structures and features relevant to knee OA that will support a broad range of existing and anticipated measurement methods while balancing requirements for high image quality and consistency against the practical considerations of a large multi-center cohort study. Strengths of the final knee MRI protocol include cartilage quantification capabilities in three planes due to multi-planar reconstruction of a thin slice, high spatial resolution 3D DESS acquisition and the multiple, non-fat-suppressed image contrasts measured during the T2 relaxation time mapping acquisition. {\textcopyright} 2008 Osteoarthritis Research Society International.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Peterfy, C. G. and Schneider, E. and Nevitt, M.},
doi = {10.1016/j.joca.2008.06.016},
eprint = {NIHMS150003},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Peterfy, Schneider, Nevitt - 2008 - The osteoarthritis initiative report on the design rationale for the magnetic resonance imaging prot.pdf:pdf},
isbn = {1522-9653 (Electronic)},
issn = {10634584},
journal = {Osteoarthritis and Cartilage},
keywords = {Knee,MRI,Osteoarthritis,Protocol},
number = {12},
pages = {1433--1441},
pmid = {18786841},
publisher = {Elsevier Ltd},
title = {{The osteoarthritis initiative: report on the design rationale for the magnetic resonance imaging protocol for the knee}},
url = {http://dx.doi.org/10.1016/j.joca.2008.06.016},
volume = {16},
year = {2008}
}
@inproceedings{Zhang2020,
abstract = {Knee osteoarthritis (OA) is a chronic degenerative disorder of joints and it is the most common reason leading to total knee joint replacement. Diagnosis of OA involves subjective judgment on symptoms, medical history, and radiographic readings using Kellgren-Lawrence grade (KL-grade). Deep learning-based methods such as Convolution Neural Networks (CNN) have recently been applied to automatically diagnose radiographic knee OA. In this study, we applied Residual Neural Network (ResNet) to first detect knee joint from radiographs and later combine ResNet with Convolutional Block Attention Module (CBAM) to make a prediction of the KL-grade automatically. The proposed model achieved a multi-class average accuracy of 74.81%, mean squared error of 0.36, and quadratic Kappa score of 0.88, which demonstrates a significant improvement over the published results. The attention maps were analyzed to provide insights on the decision process of the proposed model1.},
author = {Zhang, B and Tan, J and Cho, K and Chang, G and Deniz, Cem M},
booktitle = {2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
doi = {10.1109/ISBI45749.2020.9098456},
isbn = {1945-8452 VO -},
keywords = {CNN,Diagnostic radiography,KL grade classification,KL-grade,Kellgren-Lawrence grade,Pipelines,Predictive models,ResNet,Support vector machines,Task analysis,Training,attention maps,bone,convolution neural networks,convolutional block attention module,convolutional neural nets,convolutional neural networks,deep learning,deep learning-based methods,diagnostic radiography,diseases,feature extraction,knee,knee osteoarthritis,learning (artificial intelligence),mean squared error,medical disorders,medical history,medical image processing,multiclass average accuracy,orthopaedics,osteoarthritis,osteoarthritis initiative,radiographic knee OA,radiographic readings,radiographs,radiography,residual neural network,subjective judgment,total knee joint replacement},
pages = {731--735},
title = {{Attention-based CNN for KL Grade Classification: Data from the Osteoarthritis Initiative}},
year = {2020}
}
@article{Joseph2017,
author = {Joseph, Gabby B. and McCulloch, Charles E. and Nevitt, Michael C. and Neumann, Jan and Gersing, Alexandra S. and Kretzschmar, Martin and Schwaiger, Benedikt J. and Lynch, John A. and Heilmeier, Ursula and Lane, Nancy E. and Link, Thomas M.},
doi = {10.1002/jmri.25892},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Joseph et al. - 2018 - Tool for osteoarthritis risk prediction (TOARP) over 8 years using baseline clinical data, X-ray, and MRI Data fr.pdf:pdf},
issn = {10531807},
journal = {Journal of Magnetic Resonance Imaging},
keywords = {Cartilage T2,MRI,Osteoarthritis,Risk model},
month = {jun},
number = {6},
pages = {1517--1526},
title = {{Tool for osteoarthritis risk prediction (TOARP) over 8 years using baseline clinical data, X-ray, and MRI: Data from the osteoarthritis initiative}},
url = {http://doi.wiley.com/10.1002/jmri.25892},
volume = {47},
year = {2018}
}
@article{Leung2020,
abstract = {Background: The methods for assessing knee osteoarthritis (OA) do not provide enough comprehensive information to make robust and accurate outcome predictions. Purpose: To develop a deep learning (DL) prediction model for risk of OA progression by using knee radiographs in patients who underwent total knee replacement (TKR) and matched control patients who did not undergo TKR. Materials and Methods: In this retrospective analysis that used data from the OA Initiative, a DL model on knee radiographs was developed to predict both the likelihood of a patient undergoing TKR within 9 years and Kellgren-Lawrence (KL) grade. Study participants included a case-control matched subcohort between 45 and 79 years. Patients were matched to control patients according to age, sex, ethnicity, and body mass index. The proposed model used a transfer learning approach based on the ResNet34 architecture with sevenfold nested cross-validation. Receiver operating characteristic curve analysis and conditional logistic regression assessed model performance for predicting probability and risk of TKR compared with clinical observations and two binary outcome prediction models on the basis of radiographic readings: KL grade and OA Research Society International (OARSI) grade. Results: Evaluated were 728 participants including 324 patients (mean age, 64 years 6 8 [standard deviation]; 222 women) and 324 control patients (mean age, 64 years 6 8; 222 women). The prediction model based on DL achieved an area under the receiver operating characteristic curve (AUC) of 0.87 (95% confidence interval [CI]: 0.85, 0.90), outperforming a baseline prediction model by using KL grade with an AUC of 0.74 (95% CI: 0.71, 0.77; P,.001). The risk for TKR increased with probability that a person will undergo TKR from the DL model (odds ratio [OR], 7.7; 95% CI: 2.3, 25; P,.001), KL grade (OR, 1.92; 95% CI: 1.17, 3.13; P =.009), and OARSI grade (OR, 1.20; 95% CI: 0.41, 3.50; P =.73). Conclusion: The proposed deep learning model better predicted risk of total knee replacement in osteoarthritis than did binary outcome models by using standard grading systems.},
author = {Kevin Leung and Bofei Zhang and Jimin Tan and Yiqiu Shen and Krzysztof J Geras and James S Babb and Kyunghyun Cho and Gregory Chang and Cem M Deniz},
doi = {10.1148/radiol.2020192091},
issn = {15271315},
journal = {Radiology},
number = {3},
pages = {584--593},
pmid = {32573386},
title = {{Prediction of total knee replacement and diagnosis of osteoarthritis by using deep learning on knee radiographs: Data from the osteoarthritis initiative}},
volume = {296},
year = {2020}
}
@article{Bastick2015,
abstract = {{\textcopyright} 2015, The Author(s).Background: A previous systematic review on prognostic factors for knee osteoarthritis (OA) progression showed associations for generalized OA and hyaluronic acid levels. Knee pain, radiographic severity, sex, quadriceps strength, knee injury, and regular sport activities were not associated. It has been a decade since the literature search of that review and many studies have been performed since then investigating prognostic factors for radiographic knee OA progression. Questions/purposes: The purpose of this study is to provide an updated systematic review of available evidence regarding prognostic factors for radiographic knee OA progression. Methods: We searched for observational studies in MEDLINE and EMBASE. Key words were: knee, osteoarthritis (or arthritis, or arthrosis, or degenerative joint disease), progression (or prognosis, or precipitate, or predictive), and case-control (or cohort, or longitudinal, or follow-up). Studies fulfilling the inclusion criteria were assessed for methodologic quality according to established criteria for reviews on prognostic factors in musculoskeletal disorders. Data were extracted and results were pooled if possible or summarized according to a best-evidence synthesis. A total of 1912 additional articles were identified; 43 met our inclusion criteria. The previous review contained 36 articles, thus providing a new total of 79 articles. Seventy-two of the included articles were scored high quality, the remaining seven were low quality. Results: The pooled odds ratio (OR) of two determinants showed associations with knee OA progression: baseline knee pain (OR, 2.38 [95% CI, 1.74–3.27) and Heberden nodes (OR, 2.66 [95% CI, 1.46–8.84]). Our best-evidence synthesis showed strong evidence that varus alignment, serum hyaluronic acid, and tumor necrosis factor-$\alpha$ are associated with knee OA progression. There is strong evidence that sex, former knee injury, quadriceps strength, smoking, running, and regular performance of sports are not associated with knee OA progression. Evidence for the majority of determined associations, however, was limited, conflicting, or inconclusive. Conclusions: Baseline knee pain, presence of Heberden nodes, varus alignment, and high levels of serum markers hyaluronic acid and tumor necrosis factor-$\alpha$ predict knee OA progression. Sex, knee injury, and quadriceps strength, among others, did not predict knee OA progression. Large variation remains in definitions of knee OA and knee OA progression. Clinical studies should use more consistent definitions of these factors to facilitate data pooling by future meta-analyses.},
author = {Bastick, Alex N. and Belo, Janneke N. and Runhaar, Jos and Bierma-Zeinstra, Sita M.A.},
doi = {10.1007/s11999-015-4349-z},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Bastick et al. - 2015 - What Are the Prognostic Factors for Radiographic Progression of Knee Osteoarthritis A Meta-analysis.pdf:pdf},
issn = {15281132},
journal = {Clinical Orthopaedics and Related Research},
number = {9},
pages = {2969--2989},
title = {{What Are the Prognostic Factors for Radiographic Progression of Knee Osteoarthritis? A Meta-analysis}},
volume = {473},
year = {2015}
}
@article{Abedin2019,
abstract = {Knee osteoarthritis (KOA) is a disease that impairs knee function and causes pain. A radiologist reviews knee X-ray images and grades the severity level of the impairments according to the Kellgren and Lawrence grading scheme; a five-point ordinal scale (0–4). In this study, we used Elastic Net (EN) and Random Forests (RF) to build predictive models using patient assessment data (i.e. signs and symptoms of both knees and medication use) and a convolution neural network (CNN) trained using X-ray images only. Linear mixed effect models (LMM) were used to model the within subject correlation between the two knees. The root mean squared error for the CNN, EN, and RF models was 0.77, 0.97 and 0.94 respectively. The LMM shows similar overall prediction accuracy as the EN regression but correctly accounted for the hierarchical structure of the data resulting in more reliable inference. Useful explanatory variables were identified that could be used for patient monitoring before X-ray imaging. Our analyses suggest that the models trained for predicting the KOA severity levels achieve comparable results when modeling X-ray images and patient data. The subjectivity in the KL grade is still a primary concern.},
archivePrefix = {arXiv},
arxivId = {1908.08873},
author = {Abedin, Jaynal and Antony, Joseph and McGuinness, Kevin and Moran, Kieran and O'Connor, Noel E. and Rebholz-Schuhmann, Dietrich and Newell, John},
doi = {10.1038/s41598-019-42215-9},
eprint = {1908.08873},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Abedin et al. - 2019 - Predicting knee osteoarthritis severity comparative modeling based on patient's data and plain X-ray images.pdf:pdf},
issn = {20452322},
journal = {Scientific Reports},
number = {1},
pages = {1--11},
pmid = {30962509},
title = {{Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images}},
volume = {9},
year = {2019}
}
@article{McWilliams2011,
abstract = {Objectives (1) To develop risk prediction models for knee osteoarthritis (OA) and (2) to estimate the risk reduction that results from modification of potential risk factors. Method This was a 12-year retrospective cohort study undertaken in the general population in Nottingham, UK. Baseline risk factors were collected by questionnaire. Incident radiographic knee OA was defined by Kellgren and Lawrence (KL) score ≥2. Incident symptomatic knee OA was defined by KL ≥2 plus knee pain. Progression of knee OA was defined by KL ≥1 grade increase from baseline. A logistic regression model was used for prediction. Calibration and discrimination of the models were tested in the Osteoarthritis Initiative (OAI) population and Genetics of Osteoarthritis and Lifestyle (GOAL) population. ORs of the models were compared with those obtained from meta-analysis of existing literature. Results From a community sample of 424 people aged over 40, 3 risk prediction models were developed. These included incidence of radiographic knee OA, incidence of symptomatic knee OA and progression of knee OA. All models had good calibration and moderate discrimination power in OAI and GOAL. The ORs lied within the 95% CIs of the published studies. The risk reduction due to modifying obesity at the individual and the population levels were demonstrated. Conclusions Risk prediction of knee OA based on the well established, common modifiable risk factors has been established. The models may be used to predict the risk of knee OA, and risk reduction due to preventing a specific risk factor.},
author = {Zhang, Weiya and McWilliams, Daniel F. and Ingham, Sarah L. and Doherty, S. A. and Muthuri, Stella and Muir, Kenneth R. and Doherty, Michael},
doi = {10.1136/ard.2011.149807},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Zhang et al. - 2011 - Nottingham knee osteoarthritis risk prediction models.pdf:pdf},
issn = {0003-4967},
journal = {Annals of the Rheumatic Diseases},
month = {sep},
number = {9},
pages = {1599--1604},
title = {{Nottingham knee osteoarthritis risk prediction models}},
url = {http://ard.bmj.com/content/70/9/1599.abstract http://ard.bmj.com/cgi/doi/10.1136/ard.2011.149807},
volume = {70},
year = {2011}
}
@article{Lazzarini2017,
abstract = {Objective Knee osteoarthritis (OA) is among the higher contributors to global disability. Despite its high prevalence, currently, there is no cure for this disease. Furthermore, the available diagnostic approaches have large precision errors and low sensitivity. Therefore, there is a need for new biomarkers to correctly identify early knee OA. Method We have created an analytics pipeline based on machine learning to identify small models (having few variables) that predict the 30-months incidence of knee OA (using multiple clinical and structural OA outcome measures) in overweight middle-aged women without knee OA at baseline. The data included clinical variables, food and pain questionnaires, biochemical markers (BM) and imaging-based information. Results All the models showed high performance (AUC > 0.7) while using only a few variables. We identified both the importance of each variable within the models as well its direction. Finally, we compared the performance of two models with the state-of-the-art approaches available in the literature. Conclusions We showed the potential of applying machine learning to generate predictive models for the knee OA incidence. Imaging-based information were found particularly important in the proposed models. Furthermore, our analysis confirmed the relevance of known BM for knee OA. Overall, we propose five highly predictive small models that can be possibly adopted for an early prediction of knee OA.},
author = {Lazzarini, N. and Runhaar, J. and Bay-Jensen, A.C. and Thudium, C.S. and Bierma-Zeinstra, S.M.A. and Henrotin, Y. and Bacardit, J.},
doi = {10.1016/j.joca.2017.09.001},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Lazzarini et al. - 2017 - A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in ov.pdf:pdf},
issn = {10634584},
journal = {Osteoarthritis and Cartilage},
keywords = {Incidence,Knee osteoarthritis,Machine learning,Prediction},
month = {dec},
number = {12},
pages = {2014--2021},
publisher = {Elsevier Ltd},
title = {{A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women}},
url = {https://doi.org/10.1016/j.joca.2017.09.001 https://linkinghub.elsevier.com/retrieve/pii/S1063458417311950},
volume = {25},
year = {2017}
}
@article{Barr2016,
author = {Barr, Andrew J and Dube, Bright and Hensor, Elizabeth M A and Kingsbury, Sarah R and Peat, George and Bowes, Mike A and Sharples, Linda D and Conaghan, Philip G},
issn = {1462-0332},
journal = {Rheumatology},
keywords = {3d bone shape,active appearance modelling,knee,magnetic resonance imaging,osteoarthritis,rheumatology key messages,total knee replacement},
number = {May},
pages = {1--9},
title = {{The relationship between three-dimensional knee MRI bone shape and total knee replacement — a case control study : data from the Osteoarthritis Initiative}},
volume = {(online)},
year = {2016},
doi = {10.1093/rheumatology/kew191},
url = {https://pubmed.ncbi.nlm.nih.gov/27185958/}
}
@article{Lazzarini2017,
abstract = {Objective Knee osteoarthritis (OA) is among the higher contributors to global disability. Despite its high prevalence, currently, there is no cure for this disease. Furthermore, the available diagnostic approaches have large precision errors and low sensitivity. Therefore, there is a need for new biomarkers to correctly identify early knee OA. Method We have created an analytics pipeline based on machine learning to identify small models (having few variables) that predict the 30-months incidence of knee OA (using multiple clinical and structural OA outcome measures) in overweight middle-aged women without knee OA at baseline. The data included clinical variables, food and pain questionnaires, biochemical markers (BM) and imaging-based information. Results All the models showed high performance (AUC > 0.7) while using only a few variables. We identified both the importance of each variable within the models as well its direction. Finally, we compared the performance of two models with the state-of-the-art approaches available in the literature. Conclusions We showed the potential of applying machine learning to generate predictive models for the knee OA incidence. Imaging-based information were found particularly important in the proposed models. Furthermore, our analysis confirmed the relevance of known BM for knee OA. Overall, we propose five highly predictive small models that can be possibly adopted for an early prediction of knee OA.},
author = {Lazzarini, N. and Runhaar, J. and Bay-Jensen, A.C. and Thudium, C.S. and Bierma-Zeinstra, S.M.A. and Henrotin, Y. and Bacardit, J.},
doi = {10.1016/j.joca.2017.09.001},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Lazzarini et al. - 2017 - A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in ov.pdf:pdf},
issn = {10634584},
journal = {Osteoarthritis and Cartilage},
keywords = {Incidence,Knee osteoarthritis,Machine learning,Prediction},
month = {dec},
number = {12},
pages = {2014--2021},
publisher = {Elsevier Ltd},
title = {{A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women}},
url = {https://doi.org/10.1016/j.joca.2017.09.001 https://linkinghub.elsevier.com/retrieve/pii/S1063458417311950},
volume = {25},
year = {2017}
}
@article{Chen2019,
abstract = {Knee osteoarthritis (OA) is one major cause of activity limitation and physical disability in older adults. Early detection and intervention can help slow down the OA degeneration. Physicians' grading based on visual inspection is subjective, varied across interpreters, and highly relied on their experience. In this paper, we successively apply two deep convolutional neural networks (CNN) to automatically measure the knee OA severity, as assessed by the Kellgren-Lawrence (KL) grading system. Firstly, considering the size of knee joints distributed in X-ray images with small variability, we detect knee joints using a customized one-stage YOLOv2 network. Secondly, we fine-tune the most popular CNN models, including variants of ResNet, VGG, and DenseNet as well as InceptionV3, to classify the detected knee joint images with a novel adjustable ordinal loss. To be specific, motivated by the ordinal nature of the knee KL grading task, we assign higher penalty to misclassification with larger distance between the predicted KL grade and the real KL grade. The baseline X-ray images from the Osteoarthritis Initiative (OAI) dataset are used for evaluation. On the knee joint detection, we achieve mean Jaccard index of 0.858 and recall of 92.2\% under the Jaccard index threshold of 0.75. On the knee KL grading task, the fine-tuned VGG-19 model with the proposed ordinal loss obtains the best classification accuracy of 69.7\% and mean absolute error (MAE) of 0.344. Both knee joint detection and knee KL grading achieve state-of-the-art performance. The code, dataset, and models are released at https://github.com/PingjunChen/KneeAnalysis.},
author = {Chen, Pingjun and Gao, Linlin and Shi, Xiaoshuang and Allen, Kyle and Yang, Lin},
doi = {10.1016/j.compmedimag.2019.06.002},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Chen et al. - 2019 - Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss.pdf:pdf},
issn = {08956111},
journal = {Computerized Medical Imaging and Graphics},
keywords = {Convolutional neural network,Kellgren and Lawrence grading,Knee osteoarthritis,Ordinal loss},
month = {jul},
pages = {84--92},
pmid = {31238184},
title = {{Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0895611118304956},
volume = {75},
year = {2019}
}
@article{Tiulpin2019,
abstract = {Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate the disease modifying drug development and ultimately help to prevent millions of total joint replacement surgeries performed annually. Here, we present a multi-modal machine learning-based OA progression prediction model that utilizes raw radiographic data, clinical examination results and previous medical history of the patient. We validated this approach on an independent test set of 3,918 knee images from 2,129 subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78-0.81) and Average Precision (AP) of 0.68 (0.66-0.70). In contrast, a reference approach, based on logistic regression, yielded AUC of 0.75 (0.74-0.77) and AP of 0.62 (0.60-0.64). The proposed method could significantly improve the subject selection process for OA drug-development trials and help the development of personalized therapeutic plans.},
archivePrefix = {arXiv},
arxivId = {1904.06236},
author = {Tiulpin, Aleksei and Klein, Stefan and Bierma-Zeinstra, Sita M.A. and Thevenot, Jerome and Rahtu, Esa and Meurs, Joyce Van and Oei, Edwin H.G. and Saarakkala, Simo},
eprint = {1904.06236},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Tiulpin et al. - 2019 - Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical.pdf:pdf},
journal = {arXiv},
title = {{Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical data}},
year = {2019}
}
@article{Jamshidi2019,
abstract = {Osteoarthritis (OA) is an extremely common musculoskeletal disease. However, current guidelines are not well suited for diagnosing patients in the early stages of disease and do not discriminate patients for whom the disease might progress rapidly. The most important hurdle in OA management is identifying and classifying patients who will benefit most from treatment. Further efforts are needed in patient subgrouping and developing prediction models. Conventional statistical modelling approaches exist; however, these models are limited in the amount of information they can adequately process. Comprehensive patient-specific prediction models need to be developed. Approaches such as data mining and machine learning should aid in the development of such models. Although a challenging task, technology is now available that should enable subgrouping of patients with OA and lead to improved clinical decision-making and precision medicine.},
author = {Jamshidi, Afshin and Pelletier, Jean-Pierre and Martel-Pelletier, Johanne},
doi = {10.1038/s41584-018-0130-5},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Jamshidi, Pelletier, Martel-Pelletier - 2019 - Machine-learning-based patient-specific prediction models for knee osteoarthritis.pdf:pdf},
issn = {1759-4790},
journal = {Nature Reviews Rheumatology},
month = {jan},
number = {1},
pages = {49--60},
publisher = {Springer US},
title = {{Machine-learning-based patient-specific prediction models for knee osteoarthritis}},
url = {http://dx.doi.org/10.1038/s41584-018-0130-5 http://www.nature.com/articles/s41584-018-0130-5},
volume = {15},
year = {2019}
}
@article{Guan2020,
abstract = {Objective: To develop and evaluate deep learning (DL) risk assessment models for predicting the progression of radiographic medial joint space loss using baseline knee X-rays. Methods: Knees from the Osteoarthritis Initiative without and with progression of radiographic joint space loss (defined as ≥ 0.7 mm decrease in medial joint space width measurement between baseline and 48-month follow-up X-rays) were randomly stratified into training (1400 knees) and hold-out testing (400 knees) datasets. A DL network was trained to predict the progression of radiographic joint space loss using the baseline knee X-rays. An artificial neural network was used to develop a traditional model for predicting progression utilizing demographic and radiographic risk factors. A combined joint training model was developed using a DL network to extract information from baseline knee X-rays as a feature vector, which was further concatenated with the risk factor data vector. Area under the curve (AUC) analysis was performed using the hold-out test dataset to evaluate model performance. Results: The traditional model had an AUC of 0.660 (61.5\% sensitivity and 64.0\% specificity) for predicting progression. The DL model had an AUC of 0.799 (78.0\% sensitivity and 75.5\% specificity), which was significantly higher (P < 0.001) than the traditional model. The combined model had an AUC of 0.863 (80.5\% sensitivity and specificity), which was significantly higher than the DL (P = 0.015) and traditional (P < 0.001) models. Conclusion: DL models using baseline knee X-rays had higher diagnostic performance for predicting the progression of radiographic joint space loss than the traditional model using demographic and radiographic risk factors.},
author = {Guan, B. and Liu, F. and Haj-Mirzaian, A. and Demehri, S. and Samsonov, A. and Neogi, T. and Guermazi, A. and Kijowski, R.},
doi = {10.1016/j.joca.2020.01.010},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/Guan et al. - 2020 - Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-M.pdf:pdf},
issn = {15229653},
journal = {Osteoarthritis and Cartilage},
keywords = {Deep learning,Osteoarthritis,Radiographs,Risk assessment models},
number = {4},
pages = {428--437},
pmid = {32035934},
publisher = {Elsevier Ltd},
title = {{Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period}},
url = {https://doi.org/10.1016/j.joca.2020.01.010},
volume = {28},
year = {2020}
}
@inproceedings{Gorriz2019,
abstract = {This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST). All code from our experiments will be publicly available on the github repository: \url{https://github.com/marc-gorriz/KneeOA-CNNAttention}},
address = {London, United Kingdom},
author = {G{\'{o}}rriz, Marc and Antony, Joseph and McGuinness, Kevin and Gir{\'{o}}-i-Nieto, Xavier and O'Connor, Noel E},
booktitle = {Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning},
editor = {Cardoso, M Jorge and Feragen, Aasa and Glocker, Ben and Konukoglu, Ender and Oguz, Ipek and Unal, Gozde and Vercauteren, Tom},
file = {:Users/cemdeniz/Library/Application Support/Mendeley Desktop/Downloaded/G{\'{o}}rriz et al. - 2019 - Assessing Knee OA Severity with CNN attention-based end-to-end architectures.pdf:pdf},
pages = {197--214},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{Assessing Knee OA Severity with CNN attention-based end-to-end architectures}},
url = {http://proceedings.mlr.press/v102/gorriz19a.html},
volume = {102},
year = {2019}
}
@article{Abbas2022,
title = {Machine learning using preoperative patient factors can predict duration of surgery and length of stay for total knee arthroplasty},
journal = {International Journal of Medical Informatics},
volume = {158},
pages = {104670},
year = {2022},
issn = {1386-5056},
doi = {https://doi.org/10.1016/j.ijmedinf.2021.104670},
url = {https://www.sciencedirect.com/science/article/pii/S1386505621002963},
author = {Aazad Abbas and Jacob Mosseri and Johnathan R. Lex and Jay Toor and Bheeshma Ravi and Elias B. Khalil and Cari Whyne},
keywords = {Knee, Arthroplasty, Machine learning, Duration of surgery, Length of stay, NSQIP}
}
@article{Belete2021,
title = {Automated classification of total knee replacement prosthesis on plain film radiograph using a deep convolutional neural network},
journal = {Informatics in Medicine Unlocked},
volume = {25},
pages = {100669},
year = {2021},
issn = {2352-9148},
doi = {https://doi.org/10.1016/j.imu.2021.100669},
url = {https://www.sciencedirect.com/science/article/pii/S2352914821001544},
author = {Samuel C. Belete and Vineet Batta and Holger Kunz},
keywords = {Automated detection, Convolutional neural network, Machine learning, Total knee replacement, X-ray, Plain film radiograph}
}
@article{Tiulpin2019_2,
author={Tiulpin, Aleksei and Klein, Stefan and Bierma-Zeinstra, Sita M. A. and Thevenot, J{\'e}r{\^o}me and Rahtu, Esa and Meurs, Joyce van and Oei, Edwin H. G. and Saarakkala, Simo},
title={Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data},
journal={Scientific Reports},
year={2019},
month={Dec},
volume={9},
number={1},
pages={20038},
issn={2045-2322},
doi={10.1038/s41598-019-56527-3},
url={https://doi.org/10.1038/s41598-019-56527-3}
}
@article{Tiulpin2018,
author={Tiulpin, Aleksei and Thevenot, J{\'e}r{\^o}me and Rahtu, Esa and Lehenkari, Petri and Saarakkala, Simo},
title={Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach},
journal={Scientific Reports},
year={2018},
month={Jan},
day={29},
volume={8},
number={1},
pages={1727},
issn={2045-2322},
doi={10.1038/s41598-018-20132-7},
url={https://doi.org/10.1038/s41598-018-20132-7}
}
@article{Namiri2021,
author={Namiri, Nikan K. and Lee, Jinhee and Astuto, Bruno and Liu, Felix and Shah, Rutwik and Majumdar, Sharmila and Pedoia, Valentina},
title={Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis},
journal={Scientific Reports},
year={2021},
month={May},
day={25},
volume={11},
number={1},
pages={10915},
abstract={Osteoarthritis (OA) develops through heterogenous pathophysiologic pathways. As a result, no regulatory agency approved disease modifying OA drugs are available to date. Stratifying knees into MRI-based morphological phenotypes may provide insight into predicting future OA incidence, leading to improved inclusion criteria and efficacy of therapeutics. We trained convolutional neural networks to classify bone, meniscus/cartilage, inflammatory, and hypertrophy phenotypes in knee MRIs from participants in the Osteoarthritis Initiative (n{\thinspace}={\thinspace}4791). We investigated cross-sectional association between baseline morphological phenotypes and baseline structural OA (Kellgren Lawrence grade{\thinspace}>{\thinspace}1) and symptomatic OA. Among participants without baseline OA, we evaluated association of baseline phenotypes with 48-month incidence of structural OA and symptomatic OA. The area under the curve of bone, meniscus/cartilage, inflammatory, and hypertrophy phenotype neural network classifiers was 0.89{\thinspace}{\textpm}{\thinspace}0.01, 0.93{\thinspace}{\textpm}{\thinspace}0.03, 0.96{\thinspace}{\textpm}{\thinspace}0.02, and 0.93{\thinspace}{\textpm}{\thinspace}0.02, respectively (mean{\thinspace}{\textpm}{\thinspace}standard deviation). Among those with no baseline OA, bone phenotype (OR: 2.99 (95{\%}CI: 1.59--5.62)) and hypertrophy phenotype (OR: 5.80 (95{\%}CI: 1.82--18.5)) each respectively increased odds of developing incident structural OA and symptomatic OA at 48 months. All phenotypes except meniscus/cartilage increased odds of undergoing total knee replacement within 96 months. Artificial intelligence can rapidly stratify knees into structural phenotypes associated with incident OA and total knee replacement, which may aid in stratifying patients for clinical trials of targeted therapeutics.},
issn={2045-2322},
doi={10.1038/s41598-021-90292-6},
url={https://doi.org/10.1038/s41598-021-90292-6}}

@misc{Antony2016,
  doi = {10.48550/ARXIV.1609.02469},
  url = {https://arxiv.org/abs/1609.02469},
  author = {Antony, Joseph and McGuinness, Kevin and Connor, Noel E O and Moran, Kieran},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks},
  publisher = {arXiv},
  year = {2016},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
@misc{Antony2017,
  doi = {10.48550/ARXIV.1703.09856},
  url = {https://arxiv.org/abs/1703.09856},
  author = {Antony, Joseph and McGuinness, Kevin and Moran, Kieran and O'Connor, Noel E},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks},
  publisher = {arXiv},
  year = {2017},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
@article{Helwan2022,
author={Helwan, Abdulkader
and Azar, Danielle
and Abdellatef, Hamdan},
title={An update on the knee osteoarthritis severity grading using wide residual learning},
journal={Journal of X-Ray Science and Technology},
year={2022},
publisher={IOS Press},
volume={30},
pages={1009-1021},
keywords={Knee osteoarthritis; Kellgren Lawrence; Wide ResNet-50-2; Grad-Cam; Residual learning},
abstract={BACKGROUND: Knee Osteoarthritis (KOA) is the most common type of Osteoarthritis (OA) and it is diagnosed by physicians using a standard 0 --4 Kellgren Lawrence (KL) grading system which sets the KOA on a spectrum of 5 grades; starting from normal (0) to Severe OA (4). OBJECTIVES: In this paper, we propose a transfer learning approach of a very deep wide residual learning-based network (WRN-50-2) which is fine-tuned using X-ray plain radiographs from the Osteoarthritis Initiative (OAI) dataset to learn the KL severity grading of KOA. METHODS: We propose a data augmentation approach of OAI data to avoid data imbalance and reduce overfitting by applying it only to certain KL grades depending on their number of plain radiographs. Then we conduct experiments to test the model based on an independent testing data of original plain radiographs acquired from the OAI dataset. RESULTS: Experimental results showed good generalization power in predicting the KL grade of knee X-rays with an accuracy of 72{\%} and Precision 74{\%}. Moreover, using Grad-Cam, we also observed that network selected some distinctive features that describe the prediction of a KL grade of a knee radiograph. CONCLUSION: This study demonstrates that our proposed new model outperforms several other related works, and it can be further improved to be used to help radiologists make more accurate and precise diagnosis of KOA in future clinical practice.},
note={5},
issn={1095-9114},
doi={10.3233/XST-221190},
url={https://doi.org/10.3233/XST-221190}
}
@article{Wang2022,  
author={Wang, Yifan and Bi, Zhaori and Xie, Yuxue and Wu, Tao and Zeng, Xuan and Chen, Shuang and Zhou, Dian},  
journal={IEEE Journal of Biomedical and Health Informatics},   title={Learning From Highly Confident Samples for Automatic Knee Osteoarthritis Severity Assessment: Data From the Osteoarthritis Initiative},   year={2022},  volume={26},  number={3},  pages={1239-1250},  doi={10.1109/JBHI.2021.3102090}
}
@article{KNOAP2020_Challenge,
  title={The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images},
author = {Jukka Hirvasniemi and Jos Runhaar and Rianne A. van der Heijden and Maryam Zokaeinikoo and Mingrui Yang and Xiaojuan Li and Jimin Tan and Haresh Rengaraj Rajamohan and Yuyue Zhou and Cem M. Deniz and Francesco Caliva and Claudia Iriondo and Jinhee J. Lee and Felix Liu and Alejandro Morales Martinez and Nikan Namiri and Valentina Pedoia and Egor Panfilov and Neslihan Bayramoglu and Huy Hoang Nguyen and Miika T. Nieminen and Simo Saarakkala and Aleksei Tiulpin and Endlin Lin and Alicja Li and Vincent Li and Erik B. Dam and Akshay S. Chaudhari and Richard Kijowski and Sita Bierma-Zeinstra and Edwin H.G. Oei and Stefan Klein},

  journal={Osteoarthritis and cartilage},
  volume={31},
  number={1},
  pages={115--125},
  year={2023},
  publisher={Elsevier},
  doi={10.1016/j.joca.2022.10.001}
}


@article{Cueva2022,
AUTHOR = {Cueva, Joseph Humberto and Castillo, Darwin and Espinós-Morató, Héctor and Durán, David and Díaz, Patricia and Lakshminarayanan, Vasudevan},
TITLE = {Detection and Classification of Knee Osteoarthritis},
JOURNAL = {Diagnostics},
VOLUME = {12},
YEAR = {2022},
NUMBER = {10},
ARTICLE-NUMBER = {2362},
URL = {https://www.mdpi.com/2075-4418/12/10/2362},
PubMedID = {36292051},
ISSN = {2075-4418},
ABSTRACT = {Osteoarthritis (OA) affects nearly 240 million people worldwide. Knee OA is the most common type of arthritis, especially in older adults. Physicians measure the severity of knee OA according to the Kellgren and Lawrence (KL) scale through visual inspection of X-ray or MR images. We propose a semi-automatic CADx model based on Deep Siamese convolutional neural networks and a fine-tuned ResNet-34 to simultaneously detect OA lesions in the two knees according to the KL scale. The training was done using a public dataset, whereas the validations were performed with a private dataset. Some problems of the imbalanced dataset were solved using transfer learning. The model results average of the multi-class accuracy is 61\%, presenting better performance results for classifying classes KL-0, KL-3, and KL-4 than KL-1 and KL-2. The classification results were compared and validated using the classification of experienced radiologists.},
DOI = {10.3390/diagnostics12102362}
}
@article{Lim2019,
AUTHOR = {Lim, Jihye and Kim, Jungyoon and Cheon, Songhee},
TITLE = {A Deep Neural Network-Based Method for Early Detection of Osteoarthritis Using Statistical Data},
JOURNAL = {International Journal of Environmental Research and Public Health},
VOLUME = {16},
YEAR = {2019},
NUMBER = {7},
ARTICLE-NUMBER = {1281},
URL = {https://www.mdpi.com/1660-4601/16/7/1281},
PubMedID = {30974803},
ISSN = {1660-4601},
DOI = {10.3390/ijerph16071281}
}
@article{Hu2022,  
author={Hu, Kun and Wu, Wenhua and Li, Wei and Simic, Milena and Zomaya, Albert and Wang, Zhiyong},  journal={IEEE Transactions on Medical Imaging},   title={Adversarial Evolving Neural Network for Longitudinal Knee Osteoarthritis Prediction},   year={2022},  volume={41},  number={11},  pages={3207-3217},  doi={10.1109/TMI.2022.3181060}
}
@article {Binvignat2022,
	author = {Binvignat, Marie and Pedoia, Valentina and Butte, Atul J and Louati, Karine and Klatzmann, David and Berenbaum, Francis and Mariotti-Ferrandiz, Encarnita and Sellam, J{\'e}r{\'e}mie},
	title = {Use of machine learning in osteoarthritis research: a systematic literature review},
	volume = {8},
	number = {1},
	elocation-id = {e001998},
	year = {2022},
	doi = {10.1136/rmdopen-2021-001998},
	publisher = {BMJ Specialist Journals},
	abstract = {Objective The aim of this systematic literature review was to provide a comprehensive and exhaustive overview of the use of machine learning (ML) in the clinical care of osteoarthritis (OA).Methods A systematic literature review was performed in July 2021 using MEDLINE PubMed with key words and MeSH terms. For each selected article, the number of patients, ML algorithms used, type of data analysed, validation methods and data availability were collected.Results From 1148 screened articles, 46 were selected and analysed; most were published after 2017. Twelve articles were related to diagnosis, 7 to prediction, 4 to phenotyping, 12 to severity and 11 to progression. The number of patients included ranged from 18 to 5749. Overall, 35\% of the articles described the use of deep learning And 74\% imaging analyses. A total of 85\% of the articles involved knee OA and 15\% hip OA. No study investigated hand OA. Most of the studies involved the same cohort, with data from the OA initiative described in 46\% of the articles and the MOST and Cohort Hip and Cohort Knee cohorts in 11\% and 7\%. Data and source codes were described as publicly available respectively in 54\% and 22\% of the articles. External validation was provided in only 7\% of the articles.Conclusion This review proposes an up-to-date overview of ML approaches used in clinical OA research and will help to enhance its application in this field.All data relevant to the study are included in the article or uploaded as online supplemental information. All data relevant to the study are included in the article or uploaded as online supplemental information. Complementary data are available on request.},
	URL = {https://rmdopen.bmj.com/content/8/1/e001998},
	eprint = {https://rmdopen.bmj.com/content/8/1/e001998.full.pdf},
	journal = {RMD Open}
}
@article{Klontzas2022,
title = {Differentiation between subchondral insufficiency fractures and advanced osteoarthritis of the knee using transfer learning and an ensemble of convolutional neural networks},
journal = {Injury},
volume = {53},
number = {6},
pages = {2035-2040},
year = {2022},
issn = {0020-1383},
doi = {https://doi.org/10.1016/j.injury.2022.03.008},
url = {https://www.sciencedirect.com/science/article/pii/S0020138322001954},
author = {Michail E. Klontzas and Evangelia.E. Vassalou and George A. Kakkos and Konstantinos Spanakis and Aristeidis Zibis and Kostas Marias and Apostolos H. Karantanas},
keywords = {Deep learning, Artificial intelligence, Knee, Insufficiency fractures, Osteoarthritis},
abstract = {Purpose
Subchondral insufficiency fractures (SIF) and advanced osteoarthritis (OA) of the knee are usually seen in conjunction with bone marrow lesions (BMLs) and their differentiation may pose a significant diagnostic challenge. We aimed to develop a convolutional neural network (CNN) ensemble which could successfully differentiate between these two entities.
Materials and methods
A total of 212 knees with SIF and 102 knees with advanced OA with BMLs were retrospectively included. Coronal fat suppressed PD-w images were augmented, resized and normalized, reaching a total of 1174 images. Data was used to fine-tune three ImageNet-pretrained CNNs (VGG-16, InceptionV3 and Inception-ResNet-V2). Agreement of at least two networks was recorded as the decision of the network ensemble. Ensemble performance was compared to that of two MSK radiologists on the validation set. Receiver operating characteristics (ROC) curves and the respective areas under the curve (AUC) were used to evaluate human and machine performance.
Conclusion
A CNN ensemble was highly accurate in differentiating between SIF and OA, achieving a higher or equal performance to MSK radiologists.}
}
@article{Huo20022,
title = {Automatic Grading Assessments for Knee MRI Cartilage Defects via Self-ensembling Semi-supervised Learning with Dual-Consistency},
journal = {Medical Image Analysis},
volume = {80},
pages = {102508},
year = {2022},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2022.102508},
url = {https://www.sciencedirect.com/science/article/pii/S1361841522001554},
author = {Jiayu Huo and Xi Ouyang and Liping Si and Kai Xuan and Sheng Wang and Weiwu Yao and Ying Liu and Jia Xu and Dahong Qian and Zhong Xue and Qian Wang and Dinggang Shen and Lichi Zhang},
keywords = {Knee cartilage defect, Semi-supervised learning, Dual consistency, Attention mechanism},
abstract = {Knee cartilage defects caused by osteoarthritis are major musculoskeletal disorders, leading to joint necrosis or even disability if not intervened at early stage. Deep learning has demonstrated its effectiveness in computer-aided diagnosis, but it is time-consuming to prepare a large set of well-annotated data by experienced radiologists for model training. In this paper, we propose a semi-supervised framework to effectively use unlabeled data for better evaluation of knee cartilage defect grading. Our framework is developed based on the widely-used mean-teacher classification model, by designing a novel dual-consistency strategy to boost the consistency between the teacher and student models. The main contributions are three-fold: (1) We define an attention loss function to make the network focus on the cartilage regions, which can both achieve accurate attention masks and boost classification performance simultaneously; (2) Besides enforcing the consistency of classification results, we further design a novel attention consistency mechanism to ensure the focusing of the student and teacher networks on the same defect regions; (3) We introduce an aggregation approach to ensemble the slice-level classification outcomes for deriving the final subject-level diagnosis. Experimental results show that our proposed method can significantly improve both classification and localization performances of knee cartilage defects. Our code is available on https://github.com/King-HAW/DC-MT.}
}
@article{Schiratti2021,
author={Schiratti, Jean-Baptiste
and Dubois, R{\'e}my
and Herent, Paul
and Cahan{\'e}, David
and Dachary, Jocelyn
and Clozel, Thomas
and Wainrib, Gilles
and Keime-Guibert, Florence
and Lalande, Agnes
and Pueyo, Maria
and Guillier, Romain
and Gabarroca, Christine
and Moingeon, Philippe},
title={A deep learning method for predicting knee osteoarthritis radiographic progression from MRI},
journal={Arthritis Research {\&} Therapy},
year={2021},
month={Oct},
day={18},
volume={23},
number={1},
pages={262},
abstract={The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs.},
issn={1478-6362},
doi={10.1186/s13075-021-02634-4},
url={https://doi.org/10.1186/s13075-021-02634-4}
}
@article{Eckstein2022,
  title     = "Detection of differences in longitudinal cartilage thickness
               loss using a deep-learning automated segmentation algorithm:
               Data from the Foundation for the National Institutes of Health
               biomarkers study of the osteoarthritis initiative",
  author    = "Eckstein, Felix and Chaudhari, Akshay S and Fuerst, David and
               Gaisberger, Martin and Kemnitz, Jana and Baumgartner, Christian
               F and Konukoglu, Ender and Hunter, David J and Wirth, Wolfgang",
  abstract  = "OBJECTIVE: To study the longitudinal performance of fully
               automated cartilage segmentation in knees with radiographic
               osteoarthritis (OA), we evaluated the sensitivity to change in
               progressor knees from the Foundation for the National Institutes
               of Health OA Biomarkers Consortium between the automated and
               previously reported manual expert segmentation, and we
               determined whether differences in progression rates between
               predefined cohorts can be detected by the fully automated
               approach. METHODS: The OA Initiative Biomarker Consortium was a
               nested case-control study. Progressor knees had both medial
               tibiofemoral radiographic joint space width loss ($\geq$0.7 mm)
               and a persistent increase in Western Ontario and McMaster
               Universities Osteoarthritis Index pain scores ($\geq$9 on a
               0-100 scale) after 2 years from baseline (n = 194), whereas
               non-progressor knees did not have either of both (n = 200).
               Deep-learning automated algorithms trained on radiographic OA
               knees or knees of a healthy reference cohort (HRC) were used to
               automatically segment medial femorotibial compartment (MFTC) and
               lateral femorotibial cartilage on baseline and 2-year follow-up
               magnetic resonance imaging. Findings were compared with
               previously published manual expert segmentation. RESULTS: The
               mean $\pm$ SD MFTC cartilage loss in the progressor cohort was
               -181 $\pm$ 245 $\mu$m by manual segmentation (standardized
               response mean [SRM] -0.74), -144 $\pm$ 200 $\mu$m by the
               radiographic OA-based model (SRM -0.72), and -69 $\pm$ 231
               $\mu$m by HRC-based model segmentation (SRM -0.30). Cohen's d
               for rates of progression between progressor versus the
               non-progressor cohort was -0.84 (P < 0.001) for manual, -0.68 (P
               < 0.001) for the automated radiographic OA model, and -0.14 (P =
               0.18) for automated HRC model segmentation. CONCLUSION: A fully
               automated deep-learning segmentation approach not only displays
               similar sensitivity to change of longitudinal cartilage
               thickness loss in knee OA as did manual expert segmentation but
               also effectively differentiates longitudinal rates of loss of
               cartilage thickness between cohorts with different progression
               profiles.",
  journal   = "Arthritis Care Res. (Hoboken)",
  publisher = "Wiley",
  volume    =  74,
  number    =  6,
  pages     = "929--936",
  month     =  jun,
  year      =  2022,
  copyright = "http://creativecommons.org/licenses/by-nc/4.0/",
  language  = "en"
}
@article{Pongsakonpruttikul2022,
  title    = "Artificial intelligence assistance in radiographic detection and
              classification of knee osteoarthritis and its severity: a
              cross-sectional diagnostic study",
  author   = "Pongsakonpruttikul, N and Angthong, C and Kittichai, V and
              Chuwongin, S and Puengpipattrakul, P and Thongpat, P and
              Boonsang, S and Tongloy, T",
  abstract = "OBJECTIVE: Radiographic interpretation suffers from an
              ever-increasing workload in orthopedic and radiology departments.
              The present study applied and assessed the performance of a
              convolutional neural network designed to assist orthopedists and
              radiologists in the detection and classification of knee
              osteoarthritis from early to severe degrees in accordance with
              the Kellgren-Lawrence (KL) classification system. MATERIALS AND
              METHODS: In total, 1650 knee joint radiographs (anteroposterior
              view) were collected from the Osteoarthritis Initiative public
              resource. Two models were developed: one distinguished normal (KL
              0-I) from osteoarthritic knees (KL II-IV), and the other
              classified the severity as normal (KL 0-I), non-severe (KL II),
              or severe (KL III-IV). The regions of interest were labeled under
              the supervision of experts. Our artificial intelligence (AI)
              models were trained using the You Only Look Once version 3
              (YOLOv3) detection algorithm. RESULTS: Our first AI model using
              YOLOv3 tiny could detect and classify normal and osteoarthritic
              knees on plain knee joint radiographs with 85\% accuracy and 81\%
              mean average precision. The second AI model for classifying
              severity achieved a total accuracy of 86.7\% and mean average
              precision of 70.6\%. CONCLUSIONS: Our proposed deep learning
              models provided high accuracy and satisfactory precision for the
              detection and classification of early to severe knee
              osteoarthritis on anteroposterior radiographs. These models may
              be used as diagnostic aids by interpreting knee radiographs and
              guiding the treatment options via each osteoarthritic stage for
              related physicians and specialists.",
  journal  = "Eur. Rev. Med. Pharmacol. Sci.",
  volume   =  26,
  number   =  5,
  pages    = "1549--1558",
  month    =  mar,
  year     =  2022,
  language = "en"
}
@article{Demanse2023,
title = {Unsupervised machine-learning algorithms for the identification of clinical phenotypes in the osteoarthritis initiative database},
journal = {Seminars in Arthritis and Rheumatism},
volume = {58},
pages = {152140},
year = {2023},
issn = {0049-0172},
doi = {https://doi.org/10.1016/j.semarthrit.2022.152140},
url = {https://www.sciencedirect.com/science/article/pii/S0049017222001913},
author = {David Demanse and Franziska Saxer and Patrick Lustenberger and László B. Tankó and Philipp Nikolaus and Ilja Rasin and Damian F. Brennan and Ronenn Roubenoff and Sumehra Premji and Philip G Conaghan and Matthias Schieker},
keywords = {Knee osteoarthritis, Machine learning, Cluster analysis, Clinical phenotypes, Patient segments, Precision medicine},
abstract = {Objectives
Osteoarthritis (OA) is a complex disease comprising diverse underlying patho-mechanisms. To enable the development of effective therapies, segmentation of the heterogenous patient population is critical. This study aimed at identifying such patient clusters using two different machine learning algorithms.
Methods
Using the progression and incident cohorts of the Osteoarthritis Initiative (OAI) dataset, deep embedded clustering (DEC) and multiple factor analysis with clustering (MFAC) approaches, including 157 input-variables at baseline, were employed to differentiate specific patient profiles.
Results
DEC resulted in 5 and MFAC in 3 distinct patient phenotypes. Both identified a “comorbid” cluster with higher body mass index (BMI), relevant burden of comorbidity and low levels of physical activity. Both methods also identified a younger and physically more active cluster and an elderly cluster with functional limitations, but low disease impact. The additional two clusters identified with DEC were subgroups of the young/physically active and the elderly/physically inactive clusters. Overall pain trajectories over 9 years were stable, only the numeric rating scale (NRS) for pain showed distinct increase, while physical activity decreased in all clusters. Clusters showed different (though non-significant) trajectories of joint space changes over the follow-up period of 8 years.
Conclusion
Two different clustering approaches yielded similar patient allocations primarily separating complex “comorbid” patients from healthier subjects, the latter divided in young/physically active vs elderly/physically inactive subjects. The observed association to clinical (pain/physical activity) and structural progression could be helpful for early trial design as strategy to enrich for patients who may specifically benefit from disease-modifying treatments.}
}

@article{Perslev2022,
author = {Perslev, Mathias and Pai, Akshay and Runhaar, Jos and Igel, Christian and Dam, Erik B.},
title = {Cross-Cohort Automatic Knee MRI Segmentation With Multi-Planar U-Nets},
journal = {Journal of Magnetic Resonance Imaging},
volume = {55},
number = {6},
pages = {1650-1663},
keywords = {knee segmentation, deep learning, magnetic resonance imaging, open-source software},
doi = {https://doi.org/10.1002/jmri.27978},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jmri.27978},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.27978},
abstract = {Background Segmentation of medical image volumes is a time-consuming manual task. Automatic tools are often tailored toward specific patient cohorts, and it is unclear how they behave in other clinical settings. Purpose To evaluate the performance of the open-source Multi-Planar U-Net (MPUnet), the validated Knee Imaging Quantification (KIQ) framework, and a state-of-the-art two-dimensional (2D) U-Net architecture on three clinical cohorts without extensive adaptation of the algorithms. Study Type Retrospective cohort study. Subjects A total of 253 subjects (146 females, 107 males, ages 57 ± 12 years) from three knee osteoarthritis (OA) studies (Center for Clinical and Basic Research [CCBR], Osteoarthritis Initiative [OAI], and Prevention of OA in Overweight Females [PROOF]) with varying demographics and OA severity (64/37/24/53/2 scans of Kellgren and Lawrence [KL] grades 0–4). Field Strength/Sequence 0.18 T, 1.0 T/1.5 T, and 3 T sagittal three-dimensional fast-spin echo T1w and dual-echo steady-state sequences. Assessment All models were fit without tuning to knee magnetic resonance imaging (MRI) scans with manual segmentations from three clinical cohorts. All models were evaluated across KL grades. Statistical Tests Segmentation performance differences as measured by Dice coefficients were tested with paired, two-sided Wilcoxon signed-rank statistics with significance threshold α = 0.05. Results The MPUnet performed superior or equal to KIQ and 2D U-Net on all compartments across three cohorts. Mean Dice overlap was significantly higher for MPUnet compared to KIQ and U-Net on CCBR ( vs. and ), significantly higher than KIQ and U-Net OAI ( vs. and , and not significantly different from KIQ while significantly higher than 2D U-Net on PROOF ( vs. , , and . The MPUnet performed significantly better on KL grade 3 CCBR scans with vs. for KIQ and for 2D U-Net. Data Conclusion The MPUnet matched or exceeded the performance of state-of-the-art knee MRI segmentation models across cohorts of variable sequences and patient demographics. The MPUnet required no manual tuning making it both accurate and easy-to-use. Level of Evidence 3 Technical Efficacy Stage 2},
year = {2022}
}
@article{Abdullah2022,
author={Abdullah, S. Sheik
and Rajasekaran, M. Pallikonda},
title={Automatic detection and classification of knee osteoarthritis using deep learning approach},
journal={La radiologia medica},
year={2022},
month={Apr},
day={01},
volume={127},
number={4},
pages={398-406},
abstract={We developed a tool for locating and grading knee osteoarthritis (OA) from digital X-ray images and illustrate the possibility of deep learning techniques to predict knee OA as per the Kellgren-Lawrence (KL) grading system. The purpose of the project is to see how effectively an artificial intelligence (AI)-based deep learning approach can locate and diagnose the severity of knee OA in digital X-ray images.},
issn={1826-6983},
doi={10.1007/s11547-022-01476-7},
url={https://doi.org/10.1007/s11547-022-01476-7}
}

@article{Bonakdari2021,
author = {Hossein Bonakdari and Afshin Jamshidi and Jean-Pierre Pelletier and François Abram and Ginette Tardif and Johanne Martel-Pelletier},
title ={A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening},
journal = {Therapeutic Advances in Musculoskeletal Disease},
volume = {13},
number = {},
pages = {1759720X21993254},
year = {2021},
doi = {10.1177/1759720X21993254},
    note ={PMID: 33747150},

URL = { 
        https://doi.org/10.1177/1759720X21993254
    
},
eprint = { 
        https://doi.org/10.1177/1759720X21993254
    
}
,
    abstract = { Aim:In osteoarthritis (OA) there is a need for automated screening systems for early detection of structural progressors. We built a comprehensive machine learning (ML) model that bridges major OA risk factors and serum levels of adipokines/related inflammatory factors at baseline for early prediction of at-risk knee OA patient structural progressors over time.Methods:The patient- and gender-based model development used baseline serum levels of six adipokines, three related inflammatory factors and their ratios (36), as well as major OA risk factors [age and bone mass index (BMI)]. Subjects (677) were selected from the Osteoarthritis Initiative (OAI) progression subcohort. The probability values of being structural progressors (PVBSP) were generated using our previously published prediction model, including five baseline structural features of the knee, i.e. two X-rays and three magnetic resonance imaging variables. To identify the most important variables amongst the 47 studied in relation to PVBSP, we employed the ML feature classification methodology. Among five supervised ML algorithms, the support vector machine (SVM) demonstrated the best accuracy and use for gender-based classifiers development. Performance and sensitivity of the models were assessed. A reproducibility analysis was performed with clinical trial OA patients.Results:Feature selections revealed that the combination of age, BMI, and the ratios CRP/MCP-1 and leptin/CRP are the most important variables in predicting OA structural progressors in both genders. Classification accuracies for both genders in the testing stage (OAI) were >80\%, with the highest sensitivity of CRP/MCP-1. Reproducibility analysis showed an accuracy ⩾92\%; the ratio CRP/MCP-1 demonstrated the highest sensitivity in women and leptin/CRP in men.Conclusion:This is the first time that such a framework was built for predicting knee OA structural progressors. Using this automated ML patient- and gender-based model, early prediction of knee structural OA progression can be performed with high accuracy using only three baseline serum biomarkers and two risk factors.Plain language summaryMachine learning model for early knee osteoarthritis structural progressionKnee osteoarthritis is a well-known debilitating disease leading to reduced mobility and quality of life – the main causes of chronic invalidity. Disease evolution can be slow and span many years; however, for some individuals, the progression/evolution can be fast. Current treatments are only symptomatic and conventional diagnosis of osteoarthritis is not very effective in early identification of patients who will progress rapidly. To improve therapeutic approaches, we need a robust prediction model to stratify osteoarthritis patients at an early stage according to risk of joint structure disease progression.We hypothesize that a prediction model using a machine learning system would enable such an early identification of individuals for whom osteoarthritis knee structure will degrade rapidly. Data were from the Osteoarthritis Initiative, a National Institute of Health (United States) databank, and the robustness and generalizability of the developed model was further evaluated using osteoarthritis patients from an external cohort. Using the supervised machine learning system (support vector machine), we developed an automated patient- and gender-based model enabling an early clinical prognosis for individuals at high risk of structural progressive osteoarthritis. In brief, this model employed at baseline (when the subject sees a physician) easily obtained features consisting of the two main osteoarthritis risk factors, age and bone mass index (BMI), in addition to the serum levels of three molecules. Two of these molecules belong to a family of factors names adipokines and one to a related inflammatory factor. In brief, the model comprising a combination of age, BMI, and the ratios CRP/MCP-1 and leptin/CRP were found very robust for both genders, and the high accuracy persists when tested with an external cohort conferring the gender-based model generalizability. This study offers a new automated system for identifying early knee osteoarthritis structural progressors, which will significantly improve clinical prognosis with real time patient monitoring. }
}
@article{Zeng2021,
author={Zeng, Ke
and Hua, Yingqi
and Xu, Jing
and Zhang, Tao
and Wang, Zhuoying
and Jiang, Yafei
and Han, Jing
and Yang, Mengkai
and Shen, Jiakang
and Cai, Zhengdong},
title={Multicentre Study Using Machine Learning Methods in Clinical Diagnosis of Knee Osteoarthritis},
journal={Journal of Healthcare Engineering},
year={2021},
month={Dec},
day={03},
publisher={Hindawi},
volume={2021},
pages={1765404},
abstract={Knee osteoarthritis (OA) is one of the most common musculoskeletal disorders. OA diagnosis is currently conducted by assessing symptoms and evaluating plain radiographs, but this process suffers from the subjectivity of doctors. In this study, we retrospectively compared five commonly used machine learning methods, especially the CNN network, to predict the real-world X-ray imaging data of knee joints from two different hospitals using Kellgren-Lawrence (K-L) grade of knee OA to help doctors choose proper auxiliary tools. Furthermore, we present attention maps of CNN to highlight the radiological features affecting the network decision. Such information makes the decision process transparent for practitioners, which builds better trust towards such automatic methods and, moreover, reduces the workload of clinicians, especially for remote areas without enough medical staff.},
issn={2040-2295},
doi={10.1155/2021/1765404},
url={https://doi.org/10.1155/2021/1765404}
}

@article{Bonakdari2022,
AUTHOR = {Bonakdari, Hossein and Pelletier, Jean-Pierre and Abram, François and Martel-Pelletier, Johanne},
TITLE = {A Machine Learning Model to Predict Knee Osteoarthritis Cartilage Volume Changes over Time Using Baseline Bone Curvature},
JOURNAL = {Biomedicines},
VOLUME = {10},
YEAR = {2022},
NUMBER = {6},
ARTICLE-NUMBER = {1247},
URL = {https://www.mdpi.com/2227-9059/10/6/1247},
ISSN = {2227-9059},
ABSTRACT = {The hallmark of osteoarthritis (OA), the most prevalent musculoskeletal disease, is the loss of cartilage. By using machine learning (ML), we aimed to assess if baseline knee bone curvature (BC) could predict cartilage volume loss (CVL) at one year, and to develop a gender-based model. BC and cartilage volume were assessed on 1246 participants using magnetic resonance imaging. Variables included age, body mass index, and baseline values of eight BC regions. The outcome consisted of CVL at one year in 12 regions. Five ML methods were evaluated. Validation demonstrated very good accuracy for both genders (R &ge; 0.78), except the medial tibial plateau for the woman. In conclusion, we demonstrated, for the first time, that knee CVL at one year could be predicted using five baseline BC region values. This would benefit patients at risk of structural progressive knee OA.},
DOI = {10.3390/biomedicines10061247}
}
@article{Kokkotis2022,
author={Kokkotis, Christos
and Ntakolia, Charis
and Moustakidis, Serafeim
and Giakas, Giannis
and Tsaopoulos, Dimitrios},
title={Explainable machine learning for knee osteoarthritis diagnosis based on a novel fuzzy feature selection methodology},
journal={Physical and Engineering Sciences in Medicine},
year={2022},
month={Mar},
day={01},
volume={45},
number={1},
pages={219-229},
abstract={Knee Osteoarthritis ($\Kappa$$\Omicron$$\Alpha$) is a degenerative joint disease of the knee that results from the progressive loss of cartilage. Due to KOA's multifactorial nature and the poor understanding of its pathophysiology, there is a need for reliable tools that will reduce diagnostic errors made by clinicians. The existence of public databases has facilitated the advent of advanced analytics in KOA research however the heterogeneity of the available data along with the observed high feature dimensionality make this diagnosis task difficult. The objective of the present study is to provide a robust Feature Selection (FS) methodology that could: (i) handle the multidimensional nature of the available datasets and (ii) alleviate the defectiveness of existing feature selection techniques towards the identification of important risk factors which contribute to KOA diagnosis. For this aim, we used multidimensional data obtained from the Osteoarthritis Initiative database for individuals without or with KOA. The proposed fuzzy ensemble feature selection methodology aggregates the results of several FS algorithms (filter, wrapper and embedded ones) based on fuzzy logic. The effectiveness of the proposed methodology was evaluated using an extensive experimental setup that involved multiple competing FS algorithms and several well-known ML models. A 73.55{\%} classification accuracy was achieved by the best performing model (Random Forest classifier) on a group of twenty-one selected risk factors. Explainability analysis was finally performed to quantify the impact of the selected features on the model's output thus enhancing our understanding of the rationale behind the decision-making mechanism of the best model.},
issn={2662-4737},
doi={10.1007/s13246-022-01106-6},
url={https://doi.org/10.1007/s13246-022-01106-6}
}
@article{Joseph2022,
title = {Machine learning to predict incident radiographic knee osteoarthritis over 8 Years using combined MR imaging features, demographics, and clinical factors: data from the Osteoarthritis Initiative},
journal = {Osteoarthritis and Cartilage},
volume = {30},
number = {2},
pages = {270-279},
year = {2022},
issn = {1063-4584},
doi = {https://doi.org/10.1016/j.joca.2021.11.007},
url = {https://www.sciencedirect.com/science/article/pii/S1063458421009675},
author = {G.B. Joseph and C.E. McCulloch and M.C. Nevitt and T.M. Link and J.H. Sohn},
keywords = {Osteoarthritis, Cartilage imaging, MRI, XGboost, Machine learning},
abstract = {Summary
Objective
To develop a machine learning-based prediction model for incident radiographic osteoarthritis (OA) of the knee over 8 years using MRI-based cartilage biochemical composition and knee joint structure, demographics, and clinical predictors including muscle strength and symptoms.
Design
Individuals (n = 1,044) with baseline Kellgren Lawrence (KL) grade 0–1 in the right knee from the Osteoarthritis Initiative database were analyzed. 3T MRI at baseline was used to quantify knee cartilage T2, and Whole-Organ Magnetic Resonance Imaging Scores (WORMS) were obtained for cartilage, meniscus, and bone marrow. The outcome was set as true if a subject developed KL grade 2–4 OA in the right knee over 8 years (n = 183) and false if the subject remained at KL 0–1 over 8 years (n = 861). We developed and compared three models: Model 1: 112 predictors based on OA risk factors; Model 2: top ten predictors based on feature importance score from Model 1 and clinical relevance; Model 3: Model 2 without the imaging predictors. We compared the models using the area under the ROC curve derived from hold-out data.
Results
The 10-predictor model (Model 2, that includes cartilage and meniscus WORMS scores and cartilage T2) had a slightly lower AUC (0.772) compared to the model with 112 predictors (Model 1: AUC = 0.792, p = 0.739); and had a significantly higher AUC compared to the model without MR imaging predictors (Model 3, AUC = 0.669, p = 0.011).
Conclusions
A 10-predictor model including MRI parameters coupled with demographics, symptoms, muscle, and physical activity scores provides good prediction of incident radiographic OA over 8 years.}
}
@article{Zhao2022,
author={Zhao, Yudong
and Xia, Yu
and Kuang, Gaoyan
and Cao, Jihui
and Shen, Fu
and Zhu, Mingshuang},
title={Cross-Tissue Analysis Using Machine Learning to Identify Novel Biomarkers for Knee Osteoarthritis},
journal={Computational and Mathematical Methods in Medicine},
year={2022},
month={Jun},
day={23},
publisher={Hindawi},
volume={2022},
pages={9043300},
abstract={<i>Background.</i> Knee osteoarthritis (KOA) is a common degenerative joint disease. In this study, we aimed to identify new biomarkers of KOA to improve the accuracy of diagnosis and treatment. <i>Methods.</i> GSE98918 and GSE51588 were downloaded from the Gene Expression Omnibus database as training sets, with a total of 74 samples. Gene differences were analyzed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, and Disease Ontology enrichment analyses for the differentially expressed genes (DEGs), and GSEA enrichment analysis was carried out for the training gene set. Through least absolute shrinkage and selection operator regression analysis, the support vector machine recursive feature elimination algorithm, and gene expression screening, the range of DEGs was further reduced. Immune infiltration analysis was carried out, and the prediction results of the combined biomarker logistic regression model were verified with GSE55457. <i>Results.</i> In total, 84 DEGs were identified through differential gene expression analysis. The five biomarkers that were screened further showed significant differences in cartilage, subchondral bone, and synovial tissue. The diagnostic accuracy of the model synthesized using five biomarkers through logistic regression was better than that of a single biomarker and significantly better than that of a single clinical trait. <i>Conclusions.</i> CX3CR1, SLC7A5, ARL4C, TLR7, and MTHFD2 might be used as novel biomarkers to improve the accuracy of KOA disease diagnosis, monitor disease progression, and improve the efficacy of clinical treatment.},
issn={1748-670X},
doi={10.1155/2022/9043300},
url={https://doi.org/10.1155/2022/9043300}
}
@article{Kwon2022,
author={Kwon, Soon Bin
and Ku, Yunseo
and Han, Hyuk-Soo
and Lee, Myung Chul
and Kim, Hee Chan
and Ro, Du Hyun},
title={A machine learning-based diagnostic model associated with knee osteoarthritis severity},
journal={Scientific Reports},
year={2020},
month={Sep},
day={25},
volume={10},
number={1},
pages={15743},
abstract={Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. The study included 375 volunteers with variable KOA grades. The severity of KOA was determined using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). WOMAC scores were used to classify disease severity into three groups. A total of 1087 features were extracted from the gait data. An ANOVA and student's t-test were performed and only features that were significant were selected for inclusion in the machine learning algorithm. Three WOMAC subscales (physical function, pain and stiffness) were further divided into three classes. An ANOVA was performed to determine which selected features were significantly related to the subscales. Both linear regression models and a random forest regression was used to estimate patient the WOMAC scores. Forty-three features were selected based on ANOVA and student's t-test results. The following number of features were selected from each joint: 12 from hip, 1 feature from pelvic, 17 features from knee, 9 features from ankle, 1 feature from foot, and 3 features from spatiotemporal parameters. A significance level of{\thinspace}<{\thinspace}0.0001 and{\thinspace}<{\thinspace}0.00003 was set for the ANOVA and t-test, respectively. The physical function, pain, and stiffness subscales were related to 41, 10, and 16 features, respectively. Linear regression models showed a correlation of 0.723 and the machine learning algorithm showed a correlation of 0.741. The severity of KOA was predicted by gait analysis features, which were incorporated to develop an objective estimation model for KOA severity. The identified features may serve as a tool to guide rehabilitation and progress assessments. In addition, the estimation model presented here suggests an approach for clinical application of gait analysis data for KOA evaluation.},
issn={2045-2322},
doi={10.1038/s41598-020-72941-4},
url={https://doi.org/10.1038/s41598-020-72941-4}
}
@article{Guan2022,
author={Guan, Bochen
and Liu, Fang
and Mizaian, Arya Haj
and Demehri, Shadpour
and Samsonov, Alexey
and Guermazi, Ali
and Kijowski, Richard},
title={Deep learning approach to predict pain progression in knee osteoarthritis},
journal={Skeletal Radiology},
year={2022},
month={Feb},
day={01},
volume={51},
number={2},
pages={363-373},
abstract={To develop and evaluate deep learning (DL) risk assessment models for predicting pain progression in subjects with or at risk of knee osteoarthritis (OA).},
issn={1432-2161},
doi={10.1007/s00256-021-03773-0},
url={https://doi.org/10.1007/s00256-021-03773-0}
}
@article{Lee2022,
author={Lee, Lok Sze
and Chan, Ping Keung
and Wen, Chunyi
and Fung, Wing Chiu
and Cheung, Amy
and Chan, Vincent Wai Kwan
and Cheung, Man Hong
and Fu, Henry
and Yan, Chun Hoi
and Chiu, Kwong Yuen},
title={Artificial intelligence in diagnosis of knee osteoarthritis and prediction of arthroplasty outcomes: a review},
journal={Arthroplasty},
year={2022},
month={Mar},
day={05},
volume={4},
number={1},
pages={16},
abstract={Artificial intelligence is an emerging technology with rapid growth and increasing applications in orthopaedics. This study aimed to summarize the existing evidence and recent developments of artificial intelligence in diagnosing knee osteoarthritis and predicting outcomes of total knee arthroplasty.},
issn={2524-7948},
doi={10.1186/s42836-022-00118-7},
url={https://doi.org/10.1186/s42836-022-00118-7}
}
@article{Houserman2022,
title = {The Viability of an Artificial Intelligence/Machine Learning Prediction Model to Determine Candidates for Knee Arthroplasty},
journal = {The Journal of Arthroplasty},
year = {2022},
issn = {0883-5403},
doi = {https://doi.org/10.1016/j.arth.2022.04.003},
url = {https://www.sciencedirect.com/science/article/pii/S0883540322003941},
author = {David J. Houserman and Keith R. Berend and Adolph V. Lombardi and Chanel E. Fischetti and Erik P. Duhaime and Anant Jain and David A. Crawford},
keywords = {artificial intelligence, machine learning, total knee arthroplasty, unicompartmental knee arthroplasty, partial knee replacement, knee osteoarthritis},
abstract = {Background
The purpose of this study is to assess the viability of a knee arthroplasty prediction model using 3-view X-rays that helps determine if patients with knee pain are candidates for total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), or are not arthroplasty candidates.
Methods
Analysis was performed using radiographic and surgical data from a high-volume joint replacement practice. The dataset included 3 different X-ray views (anterior-posterior, lateral, and sunrise) for 2,767 patients along with information of whether that patient underwent an arthroplasty surgery (UKA or TKA) or not. This resulted in a dataset including 8,301 images from 2,707 patients. This dataset was then split into a training set (70\%) and holdout test set (30\%). A computer vision model was trained using a transfer learning approach. The performance of the computer vision model was evaluated on the holdout test set. Accuracy and multiclass receiver operating characteristic area under curve was used to evaluate the performance of the model.
Results
The artificial intelligence model achieved an accuracy of 87.8\% on the holdout test set and a quadratic Cohen’s kappa score of 0.811. The multiclass receiver operating characteristic area under curve score for TKA was calculated to be 0.97; for UKA a score of 0.96 and for No Surgery a score of 0.98 was achieved. An accuracy of 93.8\% was achieved for predicting Surgery versus No Surgery and 88\% for TKA versus not TKA was achieved.
Conclusion
The artificial intelligence/machine learning model demonstrated viability for predicting which patients are candidates for a UKA, TKA, or no surgical intervention.}
}
@article{Almhdie-Imjabbar2022,
author={Almhdie-Imjabbar, Ahmad
and Nguyen, Khac-Lan
and Toumi, Hechmi
and Jennane, Rachid
and Lespessailles, Eric},
title={Prediction of knee osteoarthritis progression using radiological descriptors obtained from bone texture analysis and Siamese neural networks: data from OAI and MOST cohorts},
journal={Arthritis Research {\&} Therapy},
year={2022},
month={Mar},
day={08},
volume={24},
number={1},
pages={66},
abstract={Trabecular bone texture (TBT) analysis has been identified as an imaging biomarker that provides information on trabecular bone changes due to knee osteoarthritis (KOA). In parallel with the improvement in medical imaging technologies, machine learning methods have received growing interest in the scientific osteoarthritis community to potentially provide clinicians with prognostic data from conventional knee X-ray datasets, in particular from the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST) cohorts.},
issn={1478-6362},
doi={10.1186/s13075-022-02743-8},
url={https://doi.org/10.1186/s13075-022-02743-8}
}
@article{Hu_Y2022,
author={Hu, Yong
and Tang, Jie
and Zhao, Shenghao
and Li, Ye},
title={Deep Learning-Based Multimodal 3{\thinspace}T MRI for the Diagnosis of Knee Osteoarthritis},
journal={Computational and Mathematical Methods in Medicine},
year={2022},
month={Apr},
day={29},
publisher={Hindawi},
volume={2022},
pages={7643487},
abstract={The objective of this study was to investigate the application effect of deep learning model combined with different magnetic resonance imaging (MRI) sequences in the evaluation of cartilage injury of knee osteoarthritis (KOA). Specifically, an image superresolution algorithm based on an improved multiscale wide residual network model was proposed and compared with the single-shot multibox detector (SSD) algorithm, superresolution convolutional neural network (SRCNN) algorithm, and enhanced deep superresolution (EDSR) algorithm. Meanwhile, 104 patients with KOA diagnosed with cartilage injury were selected as the research subjects and underwent MRI scans, and the diagnostic performance of different MRI sequences was analyzed using arthroscopic results as the gold standard. It was found that the image reconstructed by the model in this study was clear enough, with minimum noise and artifacts, and the overall quality was better than that processed by other algorithms. Arthroscopic analysis found that grade I and grade II lesions concentrated on patella (26) and femoral trochlear (15). In addition to involving the patella and femoral trochlea, grade III and grade IV lesions gradually developed into the medial and lateral articular cartilage. The 3D-DS-WE sequence was found to be the best sequence for diagnosing KOA injury, with high diagnostic accuracy of over 95{\%} in grade IV lesions. The consistency test showed that the 3D-DESS-WE sequence and T2<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.04980993pt" id="M1" height="6.01072pt" version="1.1" viewBox="-0.0498162 -5.96091 7.75925 6.01072" width="7.75925pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-43" d="M471 153C471 170 463 194 452 212C400 220 373 229 322 255C373 281 400 290 452 298C463 316 471 339 471 357C456 366 431 371 410 370C377 329 356 310 308 279C311 336 317 364 336 413C326 432 310 451 294 459C279 451 262 432 252 413C271 364 277 336 280 279C232 310 211 329 178 370C157 371 132 367 117 357C117 340 125 316 136 298C188 290 215 281 266 255C215 229 188 220 136 212C125 194 117 171 117 153C132 144 157 139 178 140C211 181 232 200 280 231C277 174 271 146 252 97C262 78 278 59 294 51C309 59 326 78 336 97C317 146 311 174 308 231C356 200 377 181 410 140C431 139 456 143 471 153Z"/></g></svg> mapping sequence had a strong consistency with the results of arthroscopy, and the Kappa consistency test values were 0.748 and 0.682, respectively. In conclusion, MRI based on deep learning could clearly show the cartilage lesions of KOA. Of different MRI sequences, 3D-DS-WE sequence and T2<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.04980993pt" id="M2" height="6.01072pt" version="1.1" viewBox="-0.0498162 -5.96091 7.75925 6.01072" width="7.75925pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="{\#}g113-43"/></g></svg> mapping sequence showed the best diagnosis results for different degrees of KOA injury.},
issn={1748-670X},
doi={10.1155/2022/7643487},
url={https://doi.org/10.1155/2022/7643487}
}
@article{Thomas2021,
  title     = "Open source software for automatic subregional assessment of
               knee cartilage degradation using quantitative {T2} relaxometry
               and deep learning",
  author    = "Thomas, Kevin A and Krzemi{\'n}ski, Dominik and Kidzi{\'n}ski,
               {\L}ukasz and Paul, Rohan and Rubin, Elka B and Halilaj, Eni and
               Black, Marianne S and Chaudhari, Akshay and Gold, Garry E and
               Delp, Scott L",
  abstract  = "OBJECTIVE: We evaluated a fully automated femoral cartilage
               segmentation model for measuring T2 relaxation values and
               longitudinal changes using multi-echo spin-echo (MESE) magnetic
               resonance imaging (MRI). We open sourced this model and
               developed a web app available at https://kl.stanford.edu into
               which users can drag and drop images to segment them
               automatically. DESIGN: We trained a neural network to segment
               femoral cartilage from MESE MRIs. Cartilage was divided into 12
               subregions along medial-lateral, superficial-deep, and
               anterior-central-posterior boundaries. Subregional T2 values and
               four-year changes were calculated using a radiologist's
               segmentations (Reader 1) and the model's segmentations. These
               were compared using 28 held-out images. A subset of 14 images
               were also evaluated by a second expert (Reader 2) for
               comparison. RESULTS: Model segmentations agreed with Reader 1
               segmentations with a Dice score of 0.85 $\pm$ 0.03. The model's
               estimated T2 values for individual subregions agreed with those
               of Reader 1 with an average Spearman correlation of 0.89 and
               average mean absolute error (MAE) of 1.34 ms. The model's
               estimated four-year change in T2 for individual subregions
               agreed with Reader 1 with an average correlation of 0.80 and
               average MAE of 1.72 ms. The model agreed with Reader 1 at least
               as closely as Reader 2 agreed with Reader 1 in terms of Dice
               score (0.85 vs. 0.75) and subregional T2 values. CONCLUSIONS:
               Assessments of cartilage health using our fully automated
               segmentation model agreed with those of an expert as closely as
               experts agreed with one another. This has the potential to
               accelerate osteoarthritis research.",
  journal   = "Cartilage",
  publisher = "SAGE Publications",
  volume    =  13,
  number    = "1\_suppl",
  pages     = "747S--756S",
  month     =  dec,
  year      =  2021,
  keywords  = "T2 map MRI; cartilage segmentation; deep learning; neural
               network; osteoarthritis",
  language  = "en"
}
@article{Kim2020,
AUTHOR = {Kim, Dong Hyun and Lee, Kyong Joon and Choi, Dongjun and Lee, Jae Ik and Choi, Han Gyeol and Lee, Yong Seuk},
TITLE = {Can Additional Patient Information Improve the Diagnostic Performance of Deep Learning for the Interpretation of Knee Osteoarthritis Severity},
JOURNAL = {Journal of Clinical Medicine},
VOLUME = {9},
YEAR = {2020},
NUMBER = {10},
ARTICLE-NUMBER = {3341},
URL = {https://www.mdpi.com/2077-0383/9/10/3341},
PubMedID = {33080993},
ISSN = {2077-0383},
DOI = {10.3390/jcm9103341}
}
@article{Tiwari2022, 
AUTHOR = {Tiwari, Anjali and Poduval, Murali and Bagaria, Vaibhav},
TITLE = {Evaluation of artificial intelligence models for osteoarthritis of the knee using deep learning algorithms for orthopedic radiographs},
JOURNAL = {World Journal Orthopedics},
VOLUME = {13},
YEAR = {2022},
NUMBER = {6},
URL = {https://www.wjgnet.com/2218-5836/full/v13/i6/603.htm},
ISSN = {603-614},
DOI = {10.5312/wjo.v13.i6.603}
}
@article{Li_D2022,
  title     = "Sex imbalance produces biased deep learning models for knee
               osteoarthritis detection",
  author    = "Li, David and Bharti, Beepul and Wei, Jinchi and Sulam, Jeremias
               and Yi, Paul H",
  journal   = "Can. Assoc. Radiol. J.",
  publisher = "SAGE Publications",
  pages     = "8465371221120539",
  month     =  aug,
  year      =  2022,
  language  = "en"
}
@article{Wang2021,
author={Wang, Yifan
and Wang, Xianan
and Gao, Tianning
and Du, Le
and Liu, Wei},
title={An Automatic Knee Osteoarthritis Diagnosis Method Based on Deep Learning: Data from the Osteoarthritis Initiative},
journal={Journal of Healthcare Engineering},
year={2021},
month={Sep},
day={27},
publisher={Hindawi},
volume={2021},
pages={5586529},
abstract={Osteoarthritis (OA) is the most common form of arthritis. According to the evidence presented on both sides of the knee bones, radiologists assess the severity of OA based on the Kellgren{\&}{\#}x2013;Lawrence (KL) grading system. Recently, computer-aided methods are proposed to improve the efficiency of OA diagnosis. However, the human interventions required by previous semiautomatic segmentation methods limit the application on large-scale datasets. Moreover, well-known CNN architectures applied to the OA severity assessment do not explore the relations between different local regions. In this work, by integrating the object detection model, YOLO, with the visual transformer into the diagnosis procedure, we reduce human intervention and provide an end-to-end approach to automatic osteoarthritis diagnosis. Our approach correctly segments 95.57{\&}{\#}x0025; of data at the expense of training on 200 annotated images on a large dataset that contains more than 4500 samples. Furthermore, our classification result improves the accuracy by 2.5{\&}{\#}x0025; compared to the traditional CNN architectures.},
issn={2040-2295},
doi={10.1155/2021/5586529},
url={https://doi.org/10.1155/2021/5586529}
}
@article{Xiongfeng2022,
AUTHOR={Xiongfeng, Tang and Yingzhi, Li and Xianyue, Shen and Meng, He and Bo, Chen and Deming, Guo and Yanguo, Qin},   	 
TITLE={Automated detection of knee cystic lesions on magnetic resonance imaging using deep learning},      	
JOURNAL={Frontiers in Medicine},      
VOLUME={9},           
YEAR={2022},      	  
URL={https://www.frontiersin.org/articles/10.3389/fmed.2022.928642},	
DOI={10.3389/fmed.2022.928642},      
ISSN={2296-858X},   
ABSTRACT={<sec>BackgroundCystic lesions are frequently observed in knee joint diseases and are usually associated with joint pain, degenerative disorders, or acute injury. Magnetic resonance imaging-based, artificial intelligence-assisted cyst detection is an effective method to improve the whole knee joint analysis. However, few studies have investigated this method. This study is the first attempt at auto-detection of knee cysts based on deep learning methods.</sec><sec>MethodsThis retrospective study collected data from 282 subjects with knee cysts confirmed at our institution from January to October 2021. A Squeeze-and-Excitation (SE) inception attention-based You only look once version 5 (SE-YOLOv5) model was developed based on a self-attention mechanism for knee cyst-like lesion detection and differentiation from knee effusions, both characterized by high T2-weighted signals in magnetic resonance imaging (MRI) scans. Model performance was evaluated via metrics including accuracy, precision, recall, mean average precision (mAP), F1 score, and frames per second (fps).</sec><sec>ResultsThe deep learning model could accurately identify knee MRI scans and auto-detect both obvious cyst lesions and small ones with inconspicuous contrasts. The SE-YOLO V5 model constructed in this study yielded superior performance (F1 = 0.879, precision = 0.887, recall = 0.872, all class mAP0.5 = 0.944, effusion mAP = 0.945, cyst mAP = 0.942) and improved detection speed compared to a traditional YOLO model.</sec><sec>ConclusionThis proof-of-concept study examined whether deep learning models could detect knee cysts and distinguish them from knee effusions. The results demonstrated that the classical Yolo V5 and proposed SE-Yolo V5 models could accurately identify cysts.</sec>}
}
@article{Joseph2022_2,
author={Joseph, Gabby B.
and McCulloch, Charles E.
and Sohn, Jae Ho
and Pedoia, Valentina
and Majumdar, Sharmila
and Link, Thomas M.},
title={AI MSK clinical applications: cartilage and osteoarthritis},
journal={Skeletal Radiology},
year={2022},
month={Feb},
day={01},
volume={51},
number={2},
pages={331-343},
abstract={The advancements of artificial intelligence (AI) for osteoarthritis (OA) applications have been rapid in recent years, particularly innovations of deep learning for image classification, lesion detection, cartilage segmentation, and prediction modeling of future knee OA development. This review article focuses on AI applications in OA research, first describing machine learning (ML) techniques and workflow, followed by how these algorithms are used for OA classification tasks through imaging and non-imaging-based ML models. Deep learning applications for OA research, including analysis of both radiographs for automatic detection of OA severity, and MR images for detection of cartilage/meniscus lesions and cartilage segmentation for automatic T2 quantification will be described. In addition, information on ML models that identify individuals at high risk of OA development will be provided. The future vision of machine learning applications in imaging of OA and cartilage hinges on implementation of AI for optimizing imaging protocols, quantitative assessment of cartilage, and automated analysis of disease burden yielding a faster and more efficient workflow for a radiologist with a higher level of reproducibility and precision. It may also provide risk assessment tools for individual patients, which is an integral part of precision medicine.},
issn={1432-2161},
doi={10.1007/s00256-021-03909-2},
url={https://doi.org/10.1007/s00256-021-03909-2}
}
@article{Bayramoglu2021,
title = {Automated detection of patellofemoral osteoarthritis from knee lateral view radiographs using deep learning: data from the Multicenter Osteoarthritis Study (MOST)},
journal = {Osteoarthritis and Cartilage},
volume = {29},
number = {10},
pages = {1432-1447},
year = {2021},
issn = {1063-4584},
doi = {https://doi.org/10.1016/j.joca.2021.06.011},
url = {https://www.sciencedirect.com/science/article/pii/S1063458421008359},
author = {N. Bayramoglu and M.T. Nieminen and S. Saarakkala},
keywords = {Patellofemoral osteoarthritis, Deep learning, Radiographic PFOA prediction, Knee, Radiograph},
abstract = {Summary
Objective
To assess the ability of imaging-based deep learning to detect radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs.
Design
Knee lateral view radiographs were extracted from The Multicenter Osteoarthritis Study (MOST) public use datasets (n = 18,436 knees). Patellar region-of-interest (ROI) was first automatically detected, and subsequently, end-to-end deep convolutional neural networks (CNNs) were trained and validated to detect the status of patellofemoral OA. Patellar ROI was detected using deep-learning-based object detection method. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of classification models was assessed using the area under the receiver operating characteristic curve (ROC AUC) and the average precision (AP) obtained from the Precision-Recall (PR) curve in the stratified 5-fold cross validation setting.
Results
Of the 18,436 knees, 3,425 (19\%) had PFOA. AUC and AP for the reference model including age, sex, body mass index (BMI), the total Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and tibiofemoral Kellgren–Lawrence (KL) grade to detect PFOA were 0.806 and 0.478, respectively. The CNN model that used only image data significantly improved the classifier performance (ROC AUC = 0.958, AP = 0.862).
Conclusion
We present the first machine learning based automatic PFOA detection method. Furthermore, our deep learning based model trained on patella region from knee lateral view radiographs performs better at detecting PFOA than models based on patient characteristics and clinical assessments.}
}
@article{Astuto2021,
author = {Astuto, Bruno and Flament, Io and K.                             Namiri, Nikan and Shah, Rutwik and Bharadwaj, Upasana and M.                         Link, Thomas and D.                             Bucknor, Matthew and Pedoia, Valentina and Majumdar, Sharmila},
title = {Automatic Deep Learning–assisted Detection and Grading of                     Abnormalities in Knee MRI Studies},
journal = {Radiology: Artificial Intelligence},
volume = {3},
number = {3},
pages = {e200165},
year = {2021},
doi = {10.1148/ryai.2021200165},
URL = {   
        https://doi.org/10.1148/ryai.2021200165
},
eprint = { https://doi.org/10.1148/ryai.2021200165
}
,
    abstract = { Purpose To test the hypothesis that artificial intelligence (AI) techniques can aid in identifying and assessing lesion severity in the cartilage, bone marrow, meniscus, and anterior cruciate ligament (ACL) in the knee, improving overall MRI interreader agreement. Materials and Methods This retrospective study was conducted on 1435 knee MRI studies (n = 294 patients; mean age, 43 years ± 15 [standard deviation]; 153 women) collected within three previous studies (from 2011 to 2014). All MRI studies were acquired using high-spatial-resolution three-dimensional fast-spin-echo CUBE sequence. Three-dimensional convolutional neural networks were developed to detect the regions of interest within MRI studies and grade abnormalities of the cartilage, bone marrow, menisci, and ACL. Evaluation included sensitivity, specificity, and Cohen linear-weighted ĸ. The impact of AI-aided grading in intergrader agreement was assessed on an external dataset. Results Binary lesion sensitivity reported for all tissues was between 70\% and 88\%. Specificity ranged from 85\% to 89\%. The area under the receiver operating characteristic curve for all tissues ranged from 0.83 to 0.93. Deep learning–assisted intergrader Cohen ĸ agreement significantly improved in 10 of 16 comparisons among two attending physicians and two trainees for all tissues. Conclusion The three-dimensional convolutional neural network had high sensitivity, specificity, and accuracy for knee-lesion-severity scoring and also increased intergrader agreement when used on an external dataset. Supplemental material is available for this article. Keywords: Bone Marrow, Cartilage, Computer Aided Diagnosis (CAD), Computer Applications-3D, Computer Applications-Detection/Diagnosis, Knee, Ligaments, MR-Imaging, Neural Networks, Observer Performance, Segmentation, Statistics © RSNA, 2021 See also the commentary by Li and Chang in this issue. An earlier incorrect version of this article appeared online. This article was corrected on April 16, 2021. }
}
@article{Yi_PH2020,
title = {Automated detection & classification of knee arthroplasty using deep learning},
journal = {The Knee},
volume = {27},
number = {2},
pages = {535-542},
year = {2020},
issn = {0968-0160},
doi = {https://doi.org/10.1016/j.knee.2019.11.020},
url = {https://www.sciencedirect.com/science/article/pii/S0968016019303102},
author = {Paul H. Yi and Jinchi Wei and Tae Kyung Kim and Haris I. Sair and Ferdinand K. Hui and Gregory D. Hager and Jan Fritz and Julius K. Oni},
keywords = {Artificial intelligence, Deep learning, Neural networks, Knee Arthroplasty, Knee prosthesis},
abstract = {Background
Preoperative identification of knee arthroplasty is important for planning revision surgery. However, up to 10\% of implants are not identified prior to surgery. The purposes of this study were to develop and test the performance of a deep learning system (DLS) for the automated radiographic 1) identification of the presence or absence of a total knee arthroplasty (TKA); 2) classification of TKA vs. unicompartmental knee arthroplasty (UKA); and 3) differentiation between two different primary TKA models.
Method
We collected 237 anteroposterior (AP) knee radiographs with equal proportions of native knees, TKA, and UKA and 274 AP knee radiographs with equal proportions of two TKA models. Data augmentation was used to increase the number of images for deep convolutional neural network (DCNN) training. A DLS based on DCNNs was trained on these images. Receiver operating characteristic (ROC) curves with area under the curve (AUC) were generated. Heatmaps were created using class activation mapping (CAM) to identify image features most important for DCNN decision-making.
Results
DCNNs trained to detect TKA and distinguish between TKA and UKA both achieved AUC of 1. Heatmaps demonstrated appropriate emphasis of arthroplasty components in decision-making. The DCNN trained to distinguish between the two TKA models achieved AUC of 1. Heatmaps showed emphasis of specific unique features of the TKA model designs, such as the femoral component anterior flange shape.
Conclusions
DCNNs can accurately identify presence of TKA and distinguish between specific arthroplasty designs. This proof-of-concept could be applied towards identifying other prosthesis models and prosthesis-related complications.}
}
@article{Swiecicki2021,
title = {Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists},
journal = {Computers in Biology and Medicine},
volume = {133},
pages = {104334},
year = {2021},
issn = {0010-4825},
doi = {https://doi.org/10.1016/j.compbiomed.2021.104334},
url = {https://www.sciencedirect.com/science/article/pii/S0010482521001281},
author = {Albert Swiecicki and Nianyi Li and Jonathan O'Donnell and Nicholas Said and Jichen Yang and Richard C. Mather and William A. Jiranek and Maciej A. Mazurowski},
keywords = {Knee osteoarthritis, Deep learning, Kellgren-Lawrence, Kappa coefficient},
abstract = {A fully-automated deep learning algorithm matched performance of radiologists in assessment of knee osteoarthritis severity in radiographs using the Kellgren-Lawrence grading system.
Purpose
To develop an automated deep learning-based algorithm that jointly uses Posterior-Anterior (PA) and Lateral (LAT) views of knee radiographs to assess knee osteoarthritis severity according to the Kellgren-Lawrence grading system.
Materials and methods
We used a dataset of 9739 exams from 2802 patients from Multicenter Osteoarthritis Study (MOST). The dataset was divided into a training set of 2040 patients, a validation set of 259 patients and a test set of 503 patients. A novel deep learning-based method was utilized for assessment of knee OA in two steps: (1) localization of knee joints in the images, (2) classification according to the KL grading system. Our method used both PA and LAT views as the input to the model. The scores generated by the algorithm were compared to the grades provided in the MOST dataset for the entire test set as well as grades provided by 5 radiologists at our institution for a subset of the test set.
Results
The model obtained a multi-class accuracy of 71.90\% on the entire test set when compared to the ratings provided in the MOST dataset. The quadratic weighted Kappa coefficient for this set was 0.9066. The average quadratic weighted Kappa between all pairs of radiologists from our institution who took part in the study was 0.748. The average quadratic-weighted Kappa between the algorithm and the radiologists at our institution was 0.769.
Conclusion
The proposed model performed demonstrated equivalency of KL classification to MSK radiologists, but clearly superior reproducibility. Our model also agreed with radiologists at our institution to the same extent as the radiologists with each other. The algorithm could be used to provide reproducible assessment of knee osteoarthritis severity.}
}
@article{Olsson2021,
author={Olsson, Simon
and Akbarian, Ehsan
and Lind, Anna
and Razavian, Ali Sharif
and Gordon, Max},
title={Automating classification of osteoarthritis according to Kellgren-Lawrence in the knee using deep learning in an unfiltered adult population},
journal={BMC Musculoskeletal Disorders},
year={2021},
month={Oct},
day={02},
volume={22},
number={1},
pages={844},
abstract={Prevalence for knee osteoarthritis is rising in both Sweden and globally due to increased age and obesity in the population. This has subsequently led to an increasing demand for knee arthroplasties. Correct diagnosis and classification of a knee osteoarthritis (OA) are therefore of a great interest in following-up and planning for either conservative or operative management. Most orthopedic surgeons rely on standard weight bearing radiographs of the knee. Improving the reliability and reproducibility of these interpretations could thus be hugely beneficial. Recently, deep learning which is a form of artificial intelligence (AI), has been showing promising results in interpreting radiographic images. In this study, we aim to evaluate how well an AI can classify the severity of knee OA, using entire image series and not excluding common visual disturbances such as an implant, cast and non-degenerative pathologies.},
issn={1471-2474},
doi={10.1186/s12891-021-04722-7},
url={https://doi.org/10.1186/s12891-021-04722-7}
}
@article{Ningrum2021,
  title     = "A deep learning model to predict knee osteoarthritis based on
               nonimage longitudinal medical record",
  author    = "Ningrum, Dina Nur Anggraini and Kung, Woon-Man and Tzeng,
               I-Shiang and Yuan, Sheng-Po and Wu, Chieh-Chen and Huang, Chu-Ya
               and Muhtar, Muhammad Solihuddin and Nguyen, Phung-Anh and Li,
               Jack Yu-Chuan and Wang, Yao-Chin",
  abstract  = "PURPOSE: To develop deep learning model (Deep-KOA) that can
               predict the risk of knee osteoarthritis (KOA) within the next
               year by using the previous three years nonimage-based electronic
               medical record (EMR) data. PATIENTS AND METHODS: We randomly
               selected information of two million patients from the Taiwan
               National Health Insurance Research Database (NHIRD) from January
               1, 1999 to December 31, 2013. During the study period, 132,594
               patients were diagnosed with KOA, while 1,068,464 patients
               without KOA were chosen randomly as control. We constructed a
               feature matrix by using the three-year history of sequential
               diagnoses, drug prescriptions, age, and sex. Deep learning
               methods of convolutional neural network (CNN) and artificial
               neural network (ANN) were used together to develop a risk
               prediction model. We used the area under the receiver operating
               characteristic (AUROC), sensitivity, specificity, and precision
               to evaluate the performance of Deep-KOA. Then, we explored the
               important features using stepwise feature selection. RESULTS:
               This study included 132,594 KOA patients, 83,111 females
               (62.68\%), 49,483 males (37.32\%), mean age 64.2 years, and
               1,068,464 non-KOA patients, 545,902 females (51.09\%), 522,562
               males (48.91\%), mean age 51.00 years. The Deep-KOA achieved an
               overall AUROC, sensitivity, specificity, and precision of 0.97,
               0.89, 0.93, and 0.80 respectively. The discriminative analysis
               of Deep-KOA showed important features from several diseases such
               as disorders of the eye and adnexa, acute respiratory infection,
               other metabolic and immunity disorders, and diseases of the
               musculoskeletal and connective tissue. Age and sex were not
               found as the most discriminative features, with AUROC of 0.9593
               (-0.76\% loss) and 0.9644 (-0.25\% loss) respectively. Whereas
               medications including antacid, cough suppressant, and
               expectorants were identified as discriminative features.
               CONCLUSION: Deep-KOA was developed to predict the risk of KOA
               within one year earlier, which may provide clues for clinical
               decision support systems to target patients with high risk of
               KOA to get precision prevention program.",
  journal   = "J. Multidiscip. Healthc.",
  publisher = "Informa UK Limited",
  volume    =  14,
  pages     = "2477--2485",
  month     =  sep,
  year      =  2021,
  keywords  = "artificial intelligence; clinical decision support system;
               medical informatics application; precision medicine",
  language  = "en"
}
@article{Si_L2022,
author={Si, Liping
and Zhong, Jingyu
and Huo, Jiayu
and Xuan, Kai
and Zhuang, Zixu
and Hu, Yangfan
and Wang, Qian
and Zhang, Huan
and Yao, Weiwu},
title={Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM)},
journal={European Radiology},
year={2022},
month={Feb},
day={01},
volume={32},
number={2},
pages={1353-1361},
abstract={Our purposes were (1) to explore the methodologic quality of the studies on the deep learning in knee imaging with CLAIM criterion and (2) to offer our vision for the development of CLAIM to assure high-quality reports about the application of AI to medical imaging in knee joint.},
issn={1432-1084},
doi={10.1007/s00330-021-08190-4},
url={https://doi.org/10.1007/s00330-021-08190-4}
}
@article{Liu_F2018,
author = {Liu, Fang and Zhou, Zhaoye and Samsonov, Alexey and Blankenbaker, Donna and Larison, Will and Kanarek, Andrew and Lian, Kevin and Kambhampati, Shivkumar and Kijowski, Richard},
title = {Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection},
journal = {Radiology},
volume = {289},
number = {1},
pages = {160-169},
year = {2018},
doi = {10.1148/radiol.2018172986},
    note ={PMID: 30063195},

URL = { 
    
        https://doi.org/10.1148/radiol.2018172986
    
    

},
eprint = { 
    
        https://doi.org/10.1148/radiol.2018172986
    
    

}
,
    abstract = { PurposeTo determine the feasibility of using a deep learning approach to detect cartilage lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due to cartilage degeneration, and acute cartilage injury) within the knee joint on MR images.Materials and MethodsA fully automated deep learning–based cartilage lesion detection system was developed by using segmentation and classification convolutional neural networks (CNNs). Fat-suppressed T2-weighted fast spin-echo MRI data sets of the knee of 175 patients with knee pain were retrospectively analyzed by using the deep learning method. The reference standard for training the CNN classification was the interpretation provided by a fellowship-trained musculoskeletal radiologist of the presence or absence of a cartilage lesion within 17 395 small image patches placed on the articular surfaces of the femur and tibia. Receiver operating curve (ROC) analysis and the κ statistic were used to assess diagnostic performance and intraobserver agreement for detecting cartilage lesions for two individual evaluations performed by the cartilage lesion detection system.ResultsThe sensitivity and specificity of the cartilage lesion detection system at the optimal threshold according to the Youden index were 84.1\% and 85.2\%, respectively, for evaluation 1 and 80.5\% and 87.9\%, respectively, for evaluation 2. Areas under the ROC curve were 0.917 and 0.914 for evaluations 1 and 2, respectively, indicating high overall diagnostic accuracy for detecting cartilage lesions. There was good intraobserver agreement between the two individual evaluations, with a κ of 0.76.ConclusionThis study demonstrated the feasibility of using a fully automated deep learning–based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and good intraobserver agreement for detecting cartilage degeneration and acute cartilage injury.© RSNA, 2018Online supplemental material is available for this article. }
}
@article{Liu2019,
author = {Liu, Fang and Guan, Bochen and Zhou, Zhaoye and Samsonov, Alexey and Rosas, Humberto and Lian, Kevin and Sharma, Ruchi and Kanarek, Andrew and Kim, John and Guermazi, Ali and Kijowski, Richard},
title = {Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee                     MR Images by Using Deep Learning},
journal = {Radiology: Artificial Intelligence},
volume = {1},
number = {3},
pages = {180091},
year = {2019},
doi = {10.1148/ryai.2019180091},
    note ={PMID: 32076658},

URL = { 
    
        https://doi.org/10.1148/ryai.2019180091
    
    

},
eprint = { 
    
        https://doi.org/10.1148/ryai.2019180091
    
    

}
,
    abstract = { Purpose To investigate the feasibility of using a deep learning–based approach to detect an anterior cruciate ligament (ACL) tear within the knee joint at MRI by using arthroscopy as the reference standard. Materials and Methods A fully automated deep learning–based diagnosis system was developed by using two deep convolutional neural networks (CNNs) to isolate the ACL on MR images followed by a classification CNN to detect structural abnormalities within the isolated ligament. With institutional review board approval, sagittal proton density–weighted and fat-suppressed T2-weighted fast spin-echo MR images of the knee in 175 subjects with a full-thickness ACL tear (98 male subjects and 77 female subjects; average age, 27.5 years) and 175 subjects with an intact ACL (100 male subjects and 75 female subjects; average age, 39.4 years) were retrospectively analyzed by using the deep learning approach. Sensitivity and specificity of the ACL tear detection system and five clinical radiologists for detecting an ACL tear were determined by using arthroscopic results as the reference standard. Receiver operating characteristic (ROC) analysis and two-sided exact binomial tests were used to further assess diagnostic performance. Results The sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively. In comparison, the sensitivity of the clinical radiologists ranged between 0.96 and 0.98, while the specificity ranged between 0.90 and 0.98. There was no statistically significant difference in diagnostic performance between the ACL tear detection system and clinical radiologists at P < .05. The area under the ROC curve for the ACL tear detection system was 0.98, indicating high overall diagnostic accuracy. Conclusion There was no significant difference between the diagnostic performance of the ACL tear detection system and clinical radiologists for determining the presence or absence of an ACL tear at MRI. Keywords: Computer Aided Diagnosis (CAD), Computer Applications-Detection/Diagnosis, Knee, Ligaments, MR-Imaging, Neural Networks © RSNA, 2019 Supplemental material is available for this article. }
}

@article{Chaudhari2019,
  title     = "Rapid knee {MRI} acquisition and analysis techniques for imaging
               osteoarthritis",
  author    = "Chaudhari, Akshay S and Kogan, Feliks and Pedoia, Valentina and
               Majumdar, Sharmila and Gold, Garry E and Hargreaves, Brian A",
  abstract  = "Osteoarthritis (OA) of the knee is a major source of disability
               that has no known treatment or cure. Morphological and
               compositional MRI is commonly used for assessing the bone and
               soft tissues in the knee to enhance the understanding of OA
               pathophysiology. However, it is challenging to extend these
               imaging methods and their subsequent analysis techniques to
               study large population cohorts due to slow and inefficient
               imaging acquisition and postprocessing tools. This can create a
               bottleneck in assessing early OA changes and evaluating the
               responses of novel therapeutics. The purpose of this review
               article is to highlight recent developments in tools for
               enhancing the efficiency of knee MRI methods useful to study OA.
               Advances in efficient MRI data acquisition and reconstruction
               tools for morphological and compositional imaging, efficient
               automated image analysis tools, and hardware improvements to
               further drive efficient imaging are discussed in this review.
               For each topic, we discuss the current challenges as well as
               potential future opportunities to alleviate these challenges.
               LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.",
  journal   = "J. Magn. Reson. Imaging",
  publisher = "Wiley",
  volume    =  52,
  number    =  5,
  pages     = "1321--1339",
  month     =  nov,
  year      =  2020,
  keywords  = "compositional imaging; deep learning; morphological imaging;
               quantitative MRI; rapid MRI; segmentation",
  copyright = "http://onlinelibrary.wiley.com/termsAndConditions\#vor",
  language  = "en"
}
@article{Ebrahimkhani2020,
title = {A review on segmentation of knee articular cartilage: from conventional methods towards deep learning},
journal = {Artificial Intelligence in Medicine},
volume = {106},
pages = {101851},
year = {2020},
issn = {0933-3657},
doi = {https://doi.org/10.1016/j.artmed.2020.101851},
url = {https://www.sciencedirect.com/science/article/pii/S0933365719300144},
author = {Somayeh Ebrahimkhani and Mohamed Hisham Jaward and Flavia M. Cicuttini and Anuja Dharmaratne and Yuanyuan Wang and Alba G. Seco {de Herrera}},
keywords = {Knee osteoarthritis (OA), Articular cartilage segmentation, Magnetic resonance imaging (MRI), Medical image analysis, Deep convolutional neural network (CNN)},
abstract = {In this paper, we review the state-of-the-art approaches for knee articular cartilage segmentation from conventional techniques to deep learning (DL) based techniques. Knee articular cartilage segmentation on magnetic resonance (MR) images is of great importance in early diagnosis of osteoarthritis (OA). Besides, segmentation allows estimating the articular cartilage loss rate which is utilised in clinical practice for assessing the disease progression and morphological changes. It has been traditionally applied in quantifying longitudinal knee OA progression pattern to detect and assess the articular cartilage thickness and volume. Topics covered include various image processing algorithms and major features of different segmentation techniques, feature computations and the performance evaluation metrics. This paper is intended to provide researchers with a broad overview of the currently existing methods in the field, as well as to highlight the shortcomings and potential considerations in the application at clinical practice. The survey showed that state-of-the-art techniques based on DL outperform the other segmentation methods. The analysis of the existing methods reveals that integration of DL-based algorithms with other traditional model-based approaches has achieved the best results (mean Dice similarity coefficient (DSC) between 85.8\% and 90\%).}
}
@article{Abbas2022,
title = {Machine learning using preoperative patient factors can predict duration of surgery and length of stay for total knee arthroplasty},
journal = {International Journal of Medical Informatics},
volume = {158},
pages = {104670},
year = {2022},
issn = {1386-5056},
doi = {https://doi.org/10.1016/j.ijmedinf.2021.104670},
url = {https://www.sciencedirect.com/science/article/pii/S1386505621002963},
author = {Aazad Abbas and Jacob Mosseri and Johnathan R. Lex and Jay Toor and Bheeshma Ravi and Elias B. Khalil and Cari Whyne},
keywords = {Knee, Arthroplasty, Machine learning, Duration of surgery, Length of stay, NSQIP},
abstract = {Background
Total knee arthroplasty (TKA) is one of the most resource-intensive, high-volume surgical procedures. Two drivers of the cost of TKAs are duration of surgery (DOS) and postoperative inpatient length of stay (LOS). The ability to predict TKA DOS and LOS has substantial implications for hospital finances, scheduling, and resource allocation. The goal of this study was to predict DOS and LOS for elective unilateral TKAs using machine learning models (MLMs) based on preoperative factors.
Methods
The American College of Surgeons (ACS) National Surgical and Quality Improvement (NSQIP) database was queried for unilateral TKAs from 2014 to 2019. The dataset was split into training, validation, and testing based on year. Models (linear, tree-based, and multilayer perceptron (MLP)) were fitted to the training set in scikit-learn and PyTorch, with hyperparameters tuned on the validation set. The models were trained to minimize the mean squared error (MSE). Models with the best performance on the validation set were evaluated on the testing set according to 1) MSE, 2) buffer accuracy, and 3) classification accuracy, with results compared to a mean regressor.
Results
A total of 302,300 patients were included in this study. During validation, the PyTorch MLPs had the best MSEs for DOS (0.918) and LOS (0.715). During testing, the PyTorch MLPs similarly performed best based on MSEs for DOS (0.896) and LOS (0.690). While the scikit-learn MLP yielded the best 30-minute buffer accuracy for DOS (78.8\%), the PyTorch MLP provided the best 1-day buffer accuracy for LOS (75.2\%). Nearly all the ML models were more accurate than the mean regressors for both DOS and LOS.
Conclusion
Conventional and deep learning models performed better than mean regressors for predicting DOS and LOS of unilateral elective TKA patients based on preoperative factors. Future work should include operational factors to improve overall predictions.}
}
@article{Belete2021,
title = {Automated classification of total knee replacement prosthesis on plain film radiograph using a deep convolutional neural network},
journal = {Informatics in Medicine Unlocked},
volume = {25},
pages = {100669},
year = {2021},
issn = {2352-9148},
doi = {https://doi.org/10.1016/j.imu.2021.100669},
url = {https://www.sciencedirect.com/science/article/pii/S2352914821001544},
author = {Samuel C. Belete and Vineet Batta and Holger Kunz},
keywords = {Automated detection, Convolutional neural network, Machine learning, Total knee replacement, X-ray, Plain film radiograph},
abstract = {The identification of the make and model of a total knee replacement (TKR) is a necessary step prior to revision surgery for periprosthetic fracture, loosening, wear or infection. Current methods may fail to correctly identify the implant up to 10\% of the time. This study presents the training of a Convolutional Neural Network (CNN) to automatically identify the make and model of seven TKR implants or the absence of a TKR on plain-film radiographs. Our dataset consists of 588 anteroposterior (AP) X-rays of the knee. They were randomly divided into a train, validation and testing sets with a 50:25:25 split. A CNN based on the ResNet-18 architecture was trained with the best model selected using validation results. The final model was tested on the hold-out test dataset. The trained network demonstrated perfect accuracy in classifying a hold-out test dataset of X-rays to one of the eight labelled classes. Saliency maps demonstrated the outlines of the implants are key to a given prediction. Further research will benefit from larger datasets with more complete coverage of the possible implants. The ability to recognize that implants are outside the networks trained distribution is essential to such an algorithm operating safely in clinical practice. With these issues and limitations addressed there is potential that such an algorithm could save clinicians time and reduce instances where implants are not identified pre-operatively, simplifying re-operative cases and improving clinical outcomes.}
}
@article{Caliva2021,
author={Caliv{\`a}, Francesco
and Namiri, Nikan K.
and Dubreuil, Maureen
and Pedoia, Valentina
and Ozhinsky, Eugene
and Majumdar, Sharmila},
title={Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging},
journal={Nature Reviews Rheumatology},
year={2022},
month={Feb},
day={01},
volume={18},
number={2},
pages={112-121},
abstract={The 3D nature and soft-tissue contrast of MRI makes it an invaluable tool for osteoarthritis research, by facilitating the elucidation of disease pathogenesis and progression. The recent increasing employment of MRI has certainly been stimulated by major advances that are due to considerable investment in research, particularly related to artificial intelligence (AI). These AI-related advances are revolutionizing the use of MRI in clinical research by augmenting activities ranging from image acquisition to post-processing. Automation is key to reducing the long acquisition times of MRI, conducting large-scale longitudinal studies and quantitatively defining morphometric and other important clinical features of both soft and hard tissues in various anatomical joints. Deep learning methods have been used recently for multiple applications in the musculoskeletal field to improve understanding of osteoarthritis. Compared with labour-intensive human efforts, AI-based methods have advantages and potential in all stages of imaging, as well as post-processing steps, including aiding diagnosis and prognosis. However, AI-based methods also have limitations, including the arguably limited interpretability of AI models. Given that the AI community is highly invested in uncovering uncertainties associated with model predictions and improving their interpretability, we envision future clinical translation and progressive increase in the use of AI algorithms to support clinicians in optimizing patient care.},
issn={1759-4804},
doi={10.1038/s41584-021-00719-7},
url={https://doi.org/10.1038/s41584-021-00719-7}
}
@InProceedings{Gorriz2019,
  title = 	 {Assessing Knee OA Severity with CNN attention-based end-to-end architectures},
  author =       {{G\'orriz}, Marc and Antony, Joseph and McGuinness, Kevin and {Gir\'o-i-Nieto}, Xavier and {O'Connor}, {Noel E.}},
  booktitle = 	 {Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning},
  pages = 	 {197--214},
  year = 	 {2019},
  editor = 	 {Cardoso, M. Jorge and Feragen, Aasa and Glocker, Ben and Konukoglu, Ender and Oguz, Ipek and Unal, Gozde and Vercauteren, Tom},
  volume = 	 {102},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {08--10 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v102/gorriz19a/gorriz19a.pdf},
  url = 	 {https://proceedings.mlr.press/v102/gorriz19a.html},
  abstract = 	 {This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST). All code from our experiments will be publicly available on the github repository: \url{https://github.com/marc-gorriz/KneeOA-CNNAttention}}
}
@article{Ahmed2022,
AUTHOR = {Ahmed, Sozan Mohammed and Mstafa, Ramadhan J.},
TITLE = {A Comprehensive Survey on Bone Segmentation Techniques in Knee Osteoarthritis Research: From Conventional Methods to Deep Learning},
JOURNAL = {Diagnostics},
VOLUME = {12},
YEAR = {2022},
NUMBER = {3},
ARTICLE-NUMBER = {611},
URL = {https://www.mdpi.com/2075-4418/12/3/611},
PubMedID = {35328164},
ISSN = {2075-4418},
ABSTRACT = {Knee osteoarthritis (KOA) is a degenerative joint disease, which significantly affects middle-aged and elderly people. The majority of KOA is primarily based on hyaline cartilage change, according to medical images. However, technical bottlenecks such as noise, artifacts, and modality pose enormous challenges for an objective and efficient early diagnosis. Therefore, the correct prediction of arthritis is an essential step for effective diagnosis and the prevention of acute arthritis, where early diagnosis and treatment can assist to reduce the progression of KOA. However, predicting the development of KOA is a difficult and urgent problem that, if addressed, could accelerate the development of disease-modifying drugs, in turn helping to avoid millions of total joint replacement procedures each year. In knee joint research and clinical practice there are segmentation approaches that play a significant role in KOA diagnosis and categorization. In this paper, we seek to give an in-depth understanding of a wide range of the most recent methodologies for knee articular bone segmentation; segmentation methods allow the estimation of articular cartilage loss rate, which is utilized in clinical practice for assessing the disease progression and morphological change, ranging from traditional techniques to deep learning (DL)-based techniques. Moreover, the purpose of this work is to give researchers a general review of the currently available methodologies in the area. Therefore, it will help researchers who want to conduct research in the field of KOA, as well as highlight deficiencies and potential considerations in application in clinical practice. Finally, we highlight the diagnostic value of deep learning for future computer-aided diagnostic applications to complete this review.},
DOI = {10.3390/diagnostics12030611}
}
@article{Song2022,
title = {A novel computer-assisted diagnosis method of knee osteoarthritis based on multivariate information and deep learning modelImage 1},
journal = {Digital Signal Processing},
pages = {103863},
year = {2022},
issn = {1051-2004},
doi = {https://doi.org/10.1016/j.dsp.2022.103863},
url = {https://www.sciencedirect.com/science/article/pii/S1051200422004808},
author = {Jiangling Song and Rui Zhang},
keywords = {Vibroarthrographic signal, Basic physiological signal, Laplace distribution, Aggregated multiscale dilated convolution network, Grading detection},
abstract = {Knee Osteoarthritis (KOA) is a chronic joint disease characterized by degeneration of knee's articular cartilage. Comparing with the imaging diagnostic method, the vibroarthrographic (VAG) may be emerged as a new candidate for diagnosis of KOA in clinics. However, it is challenging for doctors to evaluate patients' condition by visually detecting VAGs due to the limited understanding about contained information. Besides, the basic physiological signals are also closely correlated with a greater risk of KOA. Based on this, we focus on studying computer-assisted diagnosis method of KOA (KOA-CAD) using multivariate information (i.e. VAGs and basic physiological signals) based on an improved deep learning model (DLM). Firstly, a new Laplace distribution-based strategy (LD-S) for classification in DLM is designed. Secondly, an aggregated multiscale dilated convolution network (AMD-CNN) is constructed to learn features from multivariate information of KOA patients. Then, a new KOA-CAD method is proposed by integrating the AMD-CNN with the LD-S to realize three CAD objectives, including the automatic KOA detection, the KOA early detection, and the KOA grading detection. Finally, the multivariate information collected clinically is applied to verify the proposed method, where the automatic detection accuracy, early detection accuracy and grading detection accuracy are 93.6\%, 92.1\%, and 84.2\% respectively.}
}
@article{Prezja2022,
author={Prezja, Fabi
and Paloneva, Juha
and P{\"o}l{\"o}nen, Ilkka
and Niinim{\"a}ki, Esko
and {\"A}yr{\"a}m{\"o}, Sami},
title={DeepFake knee osteoarthritis X-rays from generative adversarial neural networks deceive medical experts and offer augmentation potential to automatic classification},
journal={Scientific Reports},
year={2022},
month={Nov},
day={03},
volume={12},
number={1},
pages={18573},
abstract={Recent developments in deep learning have impacted medical science. However, new privacy issues and regulatory frameworks have hindered medical data sharing and collection. Deep learning is a very data-intensive process for which such regulatory limitations limit the potential for new breakthroughs and collaborations. However, generating medically accurate synthetic data can alleviate privacy issues and potentially augment deep learning pipelines. This study presents generative adversarial neural networks capable of generating realistic images of knee joint X-rays with varying osteoarthritis severity. We offer 320,000 synthetic (DeepFake) X-ray images from training with 5,556 real images. We validated our models regarding medical accuracy with 15 medical experts and for augmentation effects with an osteoarthritis severity classification task. We devised a survey of 30 real and 30 DeepFake images for medical experts. The result showed that on average, more DeepFakes were mistaken for real than the reverse. The result signified sufficient DeepFake realism for deceiving the medical experts. Finally, our DeepFakes improved classification accuracy in an osteoarthritis severity classification task with scarce real data and transfer learning. In addition, in the same classification task, we replaced all real training data with DeepFakes and suffered only a {\$}{\$}3.79{\backslash}{\%}{\$}{\$}loss from baseline accuracy in classifying real osteoarthritis X-rays.},
issn={2045-2322},
doi={10.1038/s41598-022-23081-4},
url={https://doi.org/10.1038/s41598-022-23081-4}
}
@article{Patron2022,
AUTHOR = {Patron, Anri and Annala, Leevi and Lainiala, Olli and Paloneva, Juha and Äyrämö, Sami},
TITLE = {An Automatic Method for Assessing Spiking of Tibial Tubercles Associated with Knee Osteoarthritis},
JOURNAL = {Diagnostics},
VOLUME = {12},
YEAR = {2022},
NUMBER = {11},
ARTICLE-NUMBER = {2603},
URL = {https://www.mdpi.com/2075-4418/12/11/2603},
PubMedID = {36359448},
ISSN = {2075-4418},
ABSTRACT = {Efficient and scalable early diagnostic methods for knee osteoarthritis are desired due to the disease&rsquo;s prevalence. The current automatic methods for detecting osteoarthritis using plain radiographs struggle to identify the subjects with early-stage disease. Tibial spiking has been hypothesized as a feature of early knee osteoarthritis. Previous research has demonstrated an association between knee osteoarthritis and tibial spiking, but the connection to the early-stage disease has not been investigated. We study tibial spiking as a feature of early knee osteoarthritis. Additionally, we develop a deep learning based model for detecting tibial spiking from plain radiographs. We collected and graded 913 knee radiographs for tibial spiking. We conducted two experiments: experiments A and B. In experiment A, we compared the subjects with and without tibial spiking using Mann-Whitney U-test. Experiment B consisted of developing and validating an interpretative deep learning based method for predicting tibial spiking. The subjects with tibial spiking had more severe Kellgren-Lawrence grade, medial joint space narrowing, and osteophyte score in the lateral tibial compartment. The developed method achieved an accuracy of 0.869. We find tibial spiking a promising feature in knee osteoarthritis diagnosis. Furthermore, the detection can be automatized.},
DOI = {10.3390/diagnostics12112603}
}
@INPROCEEDINGS{Sindhu2022,  
author={Sindhu, N and Mishra, Shrimad and Gowrishankar, S and Anushka, SG and Snehananda, HA and Veena, A},  
booktitle={2022 International Conference on Edge Computing and Applications (ICECAA)},   title={Prediction of Knee-Replacement using Deep-Learning Approach},   year={2022},  volume={},  number={},  pages={1213-1218},  
doi={10.1109/ICECAA55415.2022.9936520}
}
@article{Yang2022_2,
AUTHOR = {Yang, Jianfeng and Ji, Quand and Ming Ni, Quanbo and Xin, Peng and Wu, Bo and Hu, Hanwen and Zheng, Qingyuan and Wang, Yiming 
and Cheng, Long and Zhang, Guoqiang and Wang, Yan},
TITLE = {Automatic Assessment of Knee Osteoarthritis Severity in Portable Devices based on Deep Learning},
JOURNAL = {Journal of Orthopaedic Surgery and Researh},
VOLUME = {},
YEAR = {},
NUMBER = {},
ARTICLE-NUMBER = {},
URL = {https://www.researchsquare.com/article/rs-2145895/v1},
PubMedID = {},
ISSN = {},
DOI = {10.21203/rs.3.rs-2145895/v1}
}
@article{YeohQing2022,
author = {Yeoh, Pauline and Goh, Siew Li and Hasikin, Khairunnisa and Wu, Xiang and Samiappan, Dhanalakshmi and Lai, Khin Wee},
year = {2022},
month = {01},
pages = {},
title = {An Efficient Neural Network for Segmenting Multiple Joint Tissues from Knee MRI with Hyperparameter Optimization: Data from the Osteoarthritis Initiative},
journal = {SSRN Electronic Journal},
doi = {10.2139/ssrn.4226646}
}
@misc{Alexopoulos2022,
  doi = {10.48550/ARXIV.2209.01192},
  
  url = {https://arxiv.org/abs/2209.01192},
  
  author = {Alexopoulos, Anastasis and Hirvasniemi, Jukka and Tümer, Nazli},
  
  keywords = {Medical Physics (physics.med-ph), Image and Video Processing (eess.IV), FOS: Physical sciences, FOS: Physical sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
  
  title = {Early detection of knee osteoarthritis using deep learning on knee magnetic resonance images},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International}
}
@InProceedings{Castillo2022,
author="Castillo, Darwin
and Cueva, Joseph
and D{\'i}az, Patricia
and Lakshminarayanan, Vasudevan",
editor="Zambrano Vizuete, Marcelo
and Botto-Tobar, Miguel
and Diaz Cadena, Angela
and Zambrano Vizuete, Ana",
title="Diagnostic Value of Knee Osteoarthritis Through Self-learning",
booktitle="I+D for Smart Cities and Industry",
year="2023",
publisher="Springer International Publishing",
address="Cham",
pages="239--249",
abstract="Osteoarthritis (OA) is the most common chronic and progressive musculoskeletal disorder. These chronic disorders are not diagnosed early nor is the treatment adequate, resulting in challenges for health care systems. Radiography is the most widely used imaging method for OA diagnosis since it allows a two-dimensional evaluation, whose main advantages its low cost and wide availability. We present a computer-assisted diagnostic (CAD) system for OA based on the analysis of X-ray images of the knee using deep learning to automatically score the Knee OA. The scoring is based on the Kellgren-Lawrance (KL) scale. The model was implemented in PyTorch and was based on Deep Siamese convolutional neural networks and fine-tuned ResNet-34 through transfer learning for the classification task. A public dataset was used for training and validating, and a private dataset for testing. The results indicate a multiple-class accuracy of the test set of 61{\%}. The highest accuracy was obtained with KL-3 at 89{\%}. It is expected that this software will be useful for training of medical students and can be used as a second opinion for the correct prediction of OA knee diagnosis. Early diagnosis is necessary to alleviate symptoms, delay the evolution of the disease and improve the functional capacity and quality of life of the patient.",
isbn="978-3-031-11295-9"
}
@INPROCEEDINGS{Kardawi2022,  
author={Kardawi, Muhammad Yusuf and Muchtar, Kahlil and Nurdin, Yudha and Maulina, Novi and Muchallil, Sayed},  booktitle={2022 8th International Conference on Wireless and Telematics (ICWT)},   title={A Comparative Analysis of Deep Learning Models for Detection of Knee Osteoarthritis Disease through Mobile Apps},   year={2022},  volume={},  number={},  pages={1-6},  doi={10.1109/ICWT55831.2022.9935417}
}
@article{Wang_CT2022,
  title     = "Successful real-world application of an osteoarthritis
               classification deep-learning model using 9210 knees-An
               orthopedic surgeon's view",
  author    = "Wang, Cheng-Tzu and Huang, Brady and Thogiti, Nagaraju and Zhu,
               Wan-Xuan and Chang, Chih-Hung and Pao, Jwo-Luen and Lai, Feipei",
  abstract  = "This study aimed to evaluate the performance of a deep-learning
               model to evaluate knee osteoarthritis using Kellgren-Lawrence
               grading in real-life knee radiographs. A deep convolutional
               neural network model was trained using 8964 knee radiographs
               from the osteoarthritis initiative (OAI), including 962 testing
               set images. Another 246 knee radiographs from the Far Eastern
               Memorial Hospital were used for external validation. The OAI
               testing set and external validation images were evaluated by
               experienced specialists, two orthopedic surgeons, and a
               musculoskeletal radiologist. The accuracy, interobserver
               agreement, F1 score, precision, recall, specificity, and ability
               to identify surgical candidates were used to compare the
               performances of the model and specialists. Attention maps
               illustrated the interpretability of the model classification.
               The model had a 78\% accuracy and consistent interobserver
               agreement for the OAI (model-surgeon 1 К = 0.80, model-surgeon 2
               К = 0.84, model-radiologist К = 0.86) and external validation
               (model-surgeon 1 К = 0.81, model-surgeon 2 К = 0.82,
               model-radiologist К = 0.83) images. A lower interobserver
               agreement was found in the images misclassified by the model
               (model-surgeon 1 К = 0.57, model-surgeon 2 К = 0.47,
               model-radiologist К = 0.65). The model performed better than
               specialists in identifying surgical candidates
               (Kellgren-Lawrence Stages 3 and 4) with an F1 score of 0.923.
               Our model not only had comparable results with specialists with
               respect to the ability to identify surgical candidates but also
               performed consistently with open database and real-life
               radiographs. We believe the controversy of the misclassified
               knee osteoarthritis images was based on a significantly lower
               interobserver agreement.",
  journal   = "J. Orthop. Res.",
  publisher = "Wiley",
  month     =  jul,
  year      =  2022,
  keywords  = "Kellgren-Lawrence classification; deep learning; knee
               osteoarthritis; knee radiographs",
  copyright = "http://onlinelibrary.wiley.com/termsAndConditions\#vor",
  language  = "en"
}
@article{Toit2022,
title = {Automatic femoral articular cartilage segmentation using deep learning in three-dimensional ultrasound images of the knee},
journal = {Osteoarthritis and Cartilage Open},
volume = {4},
number = {3},
pages = {100290},
year = {2022},
issn = {2665-9131},
doi = {https://doi.org/10.1016/j.ocarto.2022.100290},
url = {https://www.sciencedirect.com/science/article/pii/S2665913122000589},
author = {Carla {du Toit} and Nathan Orlando and Sam Papernick and Robert Dima and Igor Gyacskov and Aaron Fenster},
keywords = {Femoral articular cartilage segmentation, 3D ultrasound knee imaging, Knee osteoarthritis, Deep learning, Convolutional neural networks},
abstract = {Abstract
Objective
This study aimed to develop a deep learning-based approach to automatically segment the femoral articular cartilage (FAC) in 3D ultrasound (US) images of the knee to increase time efficiency and decrease rater variability.
Design
Our method involved deep learning predictions on 2DUS slices sampled in the transverse plane to view the cartilage of the femoral trochlea, followed by reconstruction into a 3D surface. A 2D U-Net was modified and trained using a dataset of 200 2DUS images resliced from 20 3DUS images. Segmentation accuracy was evaluated using a holdout dataset of 50 2DUS images resliced from 5 3DUS images. Absolute and signed error metrics were computed and FAC segmentation performance was compared between rater 1 and 2 manual segmentations.
Conclusion
This study investigated the use of a modified U-Net algorithm to automatically segment the FAC in 3DUS knee images of healthy volunteers, demonstrating that this segmentation method would increase the efficiency of anterior femoral cartilage volume estimation and expedite the post-acquisition processing for 3D US images of the knee.}
}
@INPROCEEDINGS{Sivakumari2022,  
author={Sivakumari, T. and Vani, R.},  booktitle={2022 7th International Conference on Communication and Electronics Systems (ICCES)},   title={Implementation of AlexNet for Classification of Knee Osteoarthritis},   year={2022},  volume={},  number={},  pages={1405-1409},  doi={10.1109/ICCES54183.2022.9835835}
}
@article{Takeda2022,
author={Takeda, Ryutaro
and Matsumoto, Takumi
and Maenohara, Yuji
and Omata, Yasunori
and Inui, Hiroshi
and Nagase, Yuichi
and Nishikawa, Takuji
and Tanaka, Sakae},
title={Increasing trend of radiographic features of knee osteoarthritis in rheumatoid arthritis patients before total knee arthroplasty},
journal={Scientific Reports},
year={2022},
month={Jun},
day={21},
volume={12},
number={1},
pages={10452},
abstract={To investigate the trend and factors related to the occurrence of osteoarthritis (OA)-like features on knee radiographs of rheumatoid arthritis (RA) patients undergoing total knee arthroplasty (TKA) in the recent decades. To classify antero-posterior knee radiographs into `RA' and `OA-like RA' groups, a deep learning model was developed by training the network using knee radiographs of end-stage arthropathy in RA patients obtained during 2002--2005 and in primary OA patients obtained during 2007--2009. We used this model to categorize 796 knee radiographs, which were recorded in RA patients before TKA during 2006--2020, into `OA-like RA' and `RA' groups. The annual ratio of `OA-like RA' was investigated. Moreover, univariate and multivariate analyses were performed to identify the factors associated with the classification as OA-like RA using clinical data from 240 patients. The percentage of `OA-like RA' had significant increasing trend from 20.9{\%} in 2006 to 67.7{\%} in 2020. Higher body mass index, use of biologics, and lower level of C-reactive protein were identified as independent factors for `OA-like RA'. An increasing trend of knee radiographs with OA-like features was observed in RA patients in the recent decades, which might be attributed to recent advances in pharmacotherapy.},
issn={2045-2322},
doi={10.1038/s41598-022-14440-2},
url={https://doi.org/10.1038/s41598-022-14440-2}
}
@INPROCEEDINGS{Masood2022,  
author={Masood, Haider and Hassan, Eisha and Salam, Anum Abdul and Liaquat, Muwahida},  booktitle={2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)},   title={Osteo-Doc: KL-Grading of Osteoarthritis Using Deep-Learning},   year={2022},  volume={},  number={},  pages={1-6},  doi={10.1109/ICoDT255437.2022.9787470}
}
@article{Yick2022,
author = {Hin Ting Victor Yick and Ping Keung Chan and Chunyi Wen and Wing Chiu Fung and Chun Hoi Yan and Kwong Yuen Chiu},
title ={Artificial intelligence reshapes current understanding and management of osteoarthritis: A narrative review},
journal = {Journal of Orthopaedics, Trauma and Rehabilitation},
volume = {29},
number = {1},
pages = {22104917221082315},
year = {2022},
doi = {10.1177/22104917221082315},

URL = { 
        https://doi.org/10.1177/22104917221082315
    
},
eprint = { 
        https://doi.org/10.1177/22104917221082315
    
}
,
    abstract = { Current practice of osteoarthritis has its insufficiencies which researchers are tackling with artificial intelligence (AI). This article discusses three kinds of AI models, namely diagnostic models, prediction models and morphological models. Diagnostic models enhance efficiency in diagnosis by providing an automated algorithm in knee images processing. Prediction models utilize behavioral and radiological data to assess the risk of osteoarthritis before symptom onset and needs to perform surgery. Morphological models detect biomechanical changes to facilitate understanding of pathophysiology and provide personalized intervention. Through reviewing present evidence, we demonstrate that AI could assist doctors in diagnosis, predict osteoarthritis and guide future research. }
}
@INPROCEEDINGS{Wang_Z2022,  
author={Wang, Zhe and Chetouani, Aladine and Hans, Didier and Lespessailles, Eric and Jennane, Rachid},  booktitle={2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)},   title={Siamese-Gap Network for Early Detection of Knee Osteoarthritis},   year={2022},  volume={},  number={},  pages={1-4},  doi={10.1109/ISBI52829.2022.9761626}
}
@INPROCEEDINGS{Raisuddin2022,  
author={Raisuddin, Abu Mohammed and Nguyen, Huy Hoang and Tiulpin, Aleksei},  booktitle={2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)},   title={Deep Semi-Supervised Active Learning for Knee Osteoarthritis Severity Grading},   year={2022},  volume={},  number={},  pages={1-5},  doi={10.1109/ISBI52829.2022.9761668}
}
@article{Panfilov2022A,
author = {Panfilov, E. and Tiulpin, Aleksei and Nieminen, Miika and Saarakkala, Simo},
year = {2022},
month = {04},
pages = {S86-S87},
title = {RADIOGRAPHIC OSTEOARTHRITIS PROGRESSION PREDICTION VIA MULTI-MODAL IMAGING DATA AND DEEP LEARNING},
volume = {30},
journal = {Osteoarthritis and Cartilage},
doi = {10.1016/j.joca.2022.02.107}
}
@article{Deniz2022A396,
author = {Deniz, Cem and Segal, Neil and Sodha, V. and Lynch, John and Nevitt, M. and Felson, D. and Kijowski, R.},
year = {2022},
month = {04},
pages = {S300},
title = {DEEP LEARNING ANALYSIS OF BASELINE WEIGHTBEARING COMPUTED TOMORGRAPHY FOR PREDICTING RADIOGRAPHIC KNEE OSTEOARTHRITIS PROGRESSION},
volume = {30},
journal = {Osteoarthritis and Cartilage},
doi = {10.1016/j.joca.2022.02.401}
}
@article{Kessler2022,
title = {Segmentation of knee MRI data with convolutional neural networks for semi-automated three-dimensional surface-based analysis of cartilage morphology and composition},
journal = {Osteoarthritis Imaging},
volume = {2},
number = {2},
pages = {100010},
year = {2022},
issn = {2772-6541},
doi = {https://doi.org/10.1016/j.ostima.2022.100010},
url = {https://www.sciencedirect.com/science/article/pii/S2772654122000046},
author = {Dimitri A. Kessler and James W. MacKay and Stephen M. McDonnell and Robert L. Janiczek and Martin J. Graves and Joshua D. Kaggie and Fiona J. Gilbert},
keywords = {Articular cartilage, Bone, Segmentation, Musculoskeletal, Convolutional neural network, Magnetic resonance imaging (MRI)},
abstract = {Objective
To assess automatic segmentations for surface-based analysis of cartilage morphology and composition on knee magnetic resonance (MR) images.
Methods
2D and 3D U-Nets were trained on double echo steady state (DESS) images from the publicly available Osteoarthritis Initiative (OAI) dataset with femoral and tibial bone and cartilage segmentations provided by the Zuse Institute Berlin (ZIB). The U-Nets were used to perform automatic segmentation of femoral and tibial bone-cartilage structures (bone and cartilage segmentations combined into one structure) from the DESS images. T2-weighted images from the OAI dataset were registered to the DESS images and used for T2 map calculation. Using the 3D cartilage surface mapping (3D-CaSM) method, surface-based analysis of cartilage morphology (thickness) and composition (T2) was performed using both manual and network-generated segmentations from OAI ZIB testing images. Bland-Altman analyses were performed to evaluate the accuracy of the extracted cartilage thickness and T2 measurements from both U-Nets compared to manual segmentations.
Results
Bland-Altman analysis showed a mean bias [95% limits of agreement] for femoral and tibial cartilage thickness measurements ranging between -0.12 to 0.33 [-0.28, 0.96] mm with 2D U-Net and 0.07 to 0.14 [-0.14, 0.39] mm with 3D U-Net. For T2, the mean bias [95% limits of agreement] ranged between -0.16 to 1.32 [-4.71, 4.83] ms with 2D U-Net and -0.05 to 0.46 [-2.47, 3.39] ms with 3D U-Net.
Conclusions
While both 2D and 3D U-Nets exemplified the time-efficiency benefit of using deep learning methods for generating the required segmentations, segmentations from 3D U-Nets demonstrated higher accuracy in the extracted thickness and T2 features using 3D-CaSM compared to the segmentations from 2D U-Nets.}
}
@INPROCEEDINGS{Antonio2022,  
author={Antonio, Patrick Jheyd A. and Aldwayne B. Delmo, Jen and Sevilla, Rovenson V. and Angelo D. Ligayo, Michael and Montesines, Dolor L.},  booktitle={2022 International Conference on Decision Aid Sciences and Applications (DASA)},   title={Deep Transfer Network of Knee Osteoarthritis Progression Rate Classification in MR Imaging for Medical Imaging Support System},   year={2022},  volume={},  number={},  pages={285-289},  doi={10.1109/DASA54658.2022.9765065}
}
@misc{Gu2022,
  doi = {10.48550/ARXIV.2203.08914},
  
  url = {https://arxiv.org/abs/2203.08914},
  
  author = {Gu, Hanxue and Li, Keyu and Colglazier, Roy J. and Yang, Jichen and Lebhar, Michael and O'Donnell, Jonathan and Jiranek, William A. and Mather, Richard C. and French, Rob J. and Said, Nicholas and Zhang, Jikai and Park, Christine and Mazurowski, Maciej A.},
  
  keywords = {Image and Video Processing (eess.IV), Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Knee arthritis severity measurement using deep learning: a publicly available algorithm with a multi-institutional validation showing radiologist-level performance},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
@article{Akbari2022,
  title     = "A hybrid deep learning methodology for knee osteoarthritis
               diagnosis using magnetic resonance images",
  author    = "Akbari, Mohsen and Momeni, Maryam",
  journal   = "SSRN Electron. J.",
  publisher = "Elsevier BV",
  year      =  2022,
  language  = "en"
}
@article{Lee2022,
  title     = "A personalized risk model leverages {MRI-based} structural
               phenotypes and clinical factors to predict incidence of
               radiographic osteoarthritis",
  author    = "Lee, Jinhee J and Namiri, Nikan K and Astuto, Bruno and Link,
               Thomas M and Majumdar, Sharmila and Pedoia, Valentina",
  abstract  = "OBJECTIVE: Our study aimed to investigate the association
               between time to incidence of radiographic osteoarthritis (OA)
               and MRI-based structural phenotypes proposed by Rapid
               OsteoArthritis MRI Eligibility Score (ROAMES). METHODS: A
               retrospective cohort of 2,328 participants without radiographic
               OA at baseline were selected from Osteoarthritis Initiative
               study. Utilizing deep learning model, we automatically assessed
               the presence of inflammatory, meniscus/cartilage, subchondral
               bone, and hypertrophic phenotypes from MRIs acquired at
               baseline, 12-, 24-, 36-, 48-, 72-, and 96- month follow-up
               visits. In addition to four structural phenotypes, we examined
               severe knee injury history and Western Ontario and McMaster
               University Osteoarthritis Index pain score as time-dependent. We
               used Cox proportional hazards regression to analyze the
               association between four structural phenotypes and radiographic
               OA disease-free survival, both univariate and adjusted for known
               risk factors including age, sex, race, body mass index, presence
               of Heberden nodes, and knee malalignment. RESULTS: Inflammatory
               (HR: 3.37, 95\% CI: 2.45 - 4.63), meniscus/cartilage (HR: 1.55,
               95\%CI: 1.21 - 1.98) and subchondral bone (HR: 1.84, 95\%CI:
               1.63 - 2.09) phenotypes were associated with time to
               radiographic OA at p < 0.05 when adjusted for the risk factors.
               Sex was a modifier of hypertrophic phenotype association with
               time to radiographic OA. Female participants with hypertrophic
               phenotype were associated with 2.8 times higher risk of
               radiographic OA (95\% CI: 2.25 - 7.54) compared to male
               participants without hypertrophic phenotype. CONCLUSIONS: Four
               ROAMES phenotypes may contribute to time to radiographic OA
               incidence and if validated could be used as promising tool for
               personalized OA management.",
  journal   = "Arthritis Care Res. (Hoboken)",
  publisher = "Wiley",
  month     =  mar,
  year      =  2022,
  copyright = "http://onlinelibrary.wiley.com/termsAndConditions\#vor",
  language  = "en"
}
@misc{Lemay2022,
  doi = {10.48550/ARXIV.2202.07562},
  
  url = {https://arxiv.org/abs/2202.07562},
  
  author = {Lemay, Andreanne and Hoebel, Katharina and Bridge, Christopher P. and Befano, Brian and De Sanjosé, Silvia and Egemen, Diden and Rodriguez, Ana Cecilia and Schiffman, Mark and Campbell, John Peter and Kalpathy-Cramer, Jayashree},
  
  keywords = {Image and Video Processing (eess.IV), Computer Vision and Pattern Recognition (cs.CV), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Improving the repeatability of deep learning models with Monte Carlo dropout},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution 4.0 International}
}
@INPROCEEDINGS{Panfilov_Saarrakkala2022,  
author={Ching-Chung YANG},  
booktitle={Frontiers in Artificial Intelligence and Applications Ebook Volume 345: Proceedings of CECNet 2021},  
title={Classification of Knee Osteoarthritis Severity Using Modified Masks to Preprocess X-ray Images in a Deep Learning Model},  
year={2021},  
volume={},  
number={},  
pages={369 - 378},  
doi={10.3233/FAIA210424}
}
@INPROCEEDINGS{Panfilov2022_Progression,  
author={Panfilov, Egor and Saarakkala, Simo and Nieminen, Miika T. and Tiulpin, Aleksei},  booktitle={2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)},   title={Predicting Knee Osteoarthritis Progression from Structural MRI Using Deep Learning},   year={2022},  volume={},  number={},  pages={1-5},  doi={10.1109/ISBI52829.2022.9761458}
}
@misc{Zhuang2022,
  doi = {10.48550/ARXIV.2201.04318},
  
  url = {https://arxiv.org/abs/2201.04318},
  
  author = {Zhuang, Zixu and Si, Liping and Wang, Sheng and Xuan, Kai and Ouyang, Xi and Zhan, Yiqiang and Xue, Zhong and Zhang, Lichi and Shen, Dinggang and Yao, Weiwu and Wang, Qian},
  
  keywords = {Image and Video Processing (eess.IV), Computer Vision and Pattern Recognition (cs.CV), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}
@INPROCEEDINGS{Feng2021,  
author={Feng, Yibo and Liu, Jing and Zhang, Huan and Qiu, Dawei},  booktitle={2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},   title={Automated grading of knee osteoarthritis X-ray images based on attention mechanism},   year={2021},  volume={},  number={},  pages={1927-1932},  doi={10.1109/BIBM52615.2021.9669623}
}
@article{Kim_M2022,
  title     = "Deep learning-enhanced parallel imaging and simultaneous
               multislice acceleration reconstruction in knee {MRI}",
  author    = "Kim, Minwoo and Lee, Sang-Min and Park, Chankue and Lee, Dongeon
               and Kim, Kang Soo and Jeong, Hee Seok and Kim, Shinyoung and
               Choi, Min-Hyeok and Nickel, Dominik",
  abstract  = "OBJECTIVES: This study aimed to examine various combinations of
               parallel imaging (PI) and simultaneous multislice (SMS)
               acceleration imaging using deep learning (DL)-enhanced and
               conventional reconstruction. The study also aimed at comparing
               the diagnostic performance of the various combinations in
               internal knee derangement and provided a quantitative evaluation
               of image sharpness and noise using edge rise distance (ERD) and
               noise power (NP), respectively. MATERIALS AND METHODS: The data
               from adult patients who underwent knee magnetic resonance
               imaging using various DL-enhanced acquisitions between June 2021
               and January 2022 were retrospectively analyzed. The participants
               underwent conventional 2-fold PI and DL protocols with 4- to
               8-fold acceleration imaging (P2S2 [2-fold PI with 2-fold SMS],
               P3S2, and P4S2). Three readers evaluated the internal knee
               derangement and the overall image quality. The diagnostic
               performance was calculated using consensus reading as a standard
               reference, and we conducted comparative evaluations. We
               calculated the ERD and NP for quantitative evaluations of image
               sharpness and noise, respectively. Interreader and intermethod
               agreements were calculated using Fleiss $\kappa$. RESULTS: A
               total of 33 patients (mean age, 49 $\pm$ 19 years; 20 women)
               were included in this study. The diagnostic performance for
               internal knee derangement and the overall image quality were
               similar among the evaluated protocols. The NP values were
               significantly lower using the DL protocols than with
               conventional imaging ( P 0.12). Interreader and intermethod
               agreements were moderate-to-excellent ($\kappa$ = 0.574-0.838)
               and good-to-excellent ($\kappa$ = 0.755-1.000), respectively. In
               addition, the mean acquisition time was reduced by 47\% when
               using DL with P2S2, by 62\% with P3S2, and by 71\% with P4S2,
               compared with conventional P2 imaging (2 minutes and 55
               seconds). CONCLUSIONS: The combined use of DL-enhanced 8-fold
               acceleration imaging (4-fold PI with 2-fold SMS) showed
               comparable performance with conventional 2-fold PI for the
               evaluation of internal knee derangement, with a 71\% reduction
               in acquisition time.",
  journal   = "Invest. Radiol.",
  publisher = "Ovid Technologies (Wolters Kluwer Health)",
  volume    =  57,
  number    =  12,
  pages     = "826--833",
  month     =  dec,
  year      =  2022,
  language  = "en"
}
@article{Qiu2020,
title = {Super-resolution reconstruction of knee magnetic resonance imaging based on deep learning},
journal = {Computer Methods and Programs in Biomedicine},
volume = {187},
pages = {105059},
year = {2020},
issn = {0169-2607},
doi = {https://doi.org/10.1016/j.cmpb.2019.105059},
url = {https://www.sciencedirect.com/science/article/pii/S0169260719312416},
author = {Defu Qiu and Shengxiang Zhang and Ying Liu and Jianqing Zhu and Lixin Zheng},
keywords = {Convolutional neural network, Deep learning, Low-resolution image, Medical imaging, Super resolution},
abstract = {Background and objective
With the rapid development of medical imaging and intelligent diagnosis, artificial intelligence methods have become a research hotspot of radiography processing technology in recent years. The low definition of knee magnetic resonance image texture seriously affects the diagnosis of knee osteoarthritis. This paper presents a super-resolution reconstruction method to address this problem.
Methods
In this paper, we propose an efficient medical image super-resolution (EMISR) method, in which we mainly adopted three hidden layers of super-resolution convolution neural network (SRCNN) and a sub-pixel convolution layer of efficient sub-pixel convolution neural network (ESPCN). The addition of the efficient sub-pixel convolutional layer in the hidden layer and the small network replacement consisting of concatenated convolutions to address low-resolution images but not high-resolution images are important. The EMISR method also uses cascaded small convolution kernels to improve reconstruction speed and deepen the convolution neural network to improve reconstruction quality.
Results
The proposed method is tested in the public dataset IDI, and the reconstruction quality of the algorithm is higher than that of the sparse coding-based network (SCN) method, the SRCNN method, and the ESPCN method (+ 2.306 dB, + 2.540 dB, + 1.089 dB improved); moreover, the reconstruction speed is faster than its counterparts (+ 4.272 s, + 1.967 s, and + 0.073 s improved).
Conclusion
The experimental results show that our EMISR framework has improved performance and greatly reduces the number of parameters and training time. Furthermore, the reconstructed image presents more details, and the edges are more complete. Therefore, the EMISR technique provides a more powerful medical analysis in knee osteoarthritis examinations.}
}
@article{Subhas2020,
	author = {Naveen Subhas and Hongyu Li and Mingrui Yang and Carl S. Winalski and Joshua Polster and Nancy Obuchowski and Kenji Mamoto and Ruiying Liu and Chaoyi Zhang and Peizhou Huang and Sunil Kumar Gaire and Dong Liang and Bowen Shen and Xiaojuan Li and Leslie Ying},
	title = {Diagnostic interchangeability of deep convolutional neural networks reconstructed knee MR images: preliminary experience},
	journal = {Quantitative Imaging in Medicine and Surgery},
	volume = {10},
	number = {9},
	year = {2020},
	keywords = {},
	abstract = {},
	issn = {2223-4306},	url = {https://qims.amegroups.com/article/view/46929}
}
@article{Banjar2022,
  title     = "Review of quantitative knee articular cartilage {MR} imaging",
  author    = "Banjar, Mai and Horiuchi, Saya and Gedeon, David N and Yoshioka,
               Hiroshi",
  abstract  = "Osteoarthritis (OA) is one of the most prevalent disorders in
               today's society, resulting in significant socio-economic costs
               and morbidity. MRI is widely used as a non-invasive imaging tool
               for OA of the knee. However, conventional knee MRI has
               limitations to detect subtle early cartilage degeneration before
               morphological changes are visually apparent. Novel MRI pulse
               sequences for cartilage assessment have recently received
               increased attention due to newly developed compositional MRI
               techniques, including: T2 mapping, T1rho mapping, delayed
               gadolinium-enhanced MRI of cartilage (dGEMRIC), sodium MRI,
               diffusion-weighted imaging (DWI)/ diffusion tensor imaging
               (DTI), ultrashort TE (uTE), and glycosaminoglycan specific
               chemical exchange saturation transfer (gagCEST) imaging. In this
               article, we will first review these quantitative assessments.
               Then, we will discuss the variations of quantitative values of
               knee articular cartilage with cartilage layer (depth)- and angle
               (regional)-dependent approaches. Multiple MRI sequence
               techniques can discern qualitative differences in knee
               cartilage. Normal articular hyaline cartilage has a zonal
               variation in T2 relaxation times with increasing T2 values from
               the subchondral bone to the articular surface. T1rho values were
               also higher in the superficial layer than in the deep layer in
               most locations in the medial and lateral femoral condyles,
               including the weight-bearing portion. Magic angle effect on T2
               mapping is clearly observed in the both medial and lateral
               femoral condyles, especially within the deep layers. One of the
               limitations for clinical use of these compositional assessments
               is a long scan time. Recent new approaches with compressed
               sensing (CS) and MR fingerprinting (MRF) have potential to
               provide accurate and fast quantitative cartilage assessments.",
  journal   = "Magn. Reson. Med. Sci.",
  publisher = "Japanese Society for Magnetic Resonance in Medicine",
  volume    =  21,
  number    =  1,
  pages     = "29--40",
  month     =  mar,
  year      =  2022,
  keywords  = "knee cartilage; magnetic resonance imaging; quantitative",
  language  = "en"
}
@article{Teoh2022,
author={Teoh, Yun Xin
and Lai, Khin Wee
and Usman, Juliana
and Goh, Siew Li
and Mohafez, Hamidreza
and Hasikin, Khairunnisa
and Qian, Pengjiang
and Jiang, Yizhang
and Zhang, Yuanpeng
and Dhanalakshmi, Samiappan},
title={Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches},
journal={Journal of Healthcare Engineering},
year={2022},
month={Feb},
day={18},
publisher={Hindawi},
volume={2022},
pages={4138666},
abstract={Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren{\&}{\#}x2013;Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.},
issn={2040-2295},
doi={10.1155/2022/4138666},
url={https://doi.org/10.1155/2022/4138666}
}

@article{Buchlak2022,
author={Buchlak, Quinlan D.
and Clair, Joe
and Esmaili, Nazanin
and Barmare, Arshad
and Chandrasekaran, Siva},
title={Clinical outcomes associated with robotic and computer-navigated total knee arthroplasty: a machine learning-augmented systematic review},
journal={European Journal of Orthopaedic Surgery {\&} Traumatology},
year={2022},
month={Jul},
day={01},
volume={32},
number={5},
pages={915-931},
abstract={Robotic (RTKA) and computer-navigated total knee arthroplasty (CNTKA) are increasingly replacing manual techniques in orthopaedic surgery. This systematic review compared clinical outcomes associated with RTKA and CNTKA and investigated the utility of natural language processing (NLP) for the literature synthesis.},
issn={1432-1068},
doi={10.1007/s00590-021-03059-0},
url={https://doi.org/10.1007/s00590-021-03059-0}
}
@article{Khalid2020,
AUTHOR = {Khalid, Hafsa and Hussain, Muzammil and Al Ghamdi, Mohammed A. and Khalid, Tayyaba and Khalid, Khadija and Khan, Muhammad Adnan and Fatima, Kalsoom and Masood, Khalid and Almotiri, Sultan H. and Farooq, Muhammad Shoaib and Ahmed, Aqsa},
TITLE = {A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies},
JOURNAL = {Diagnostics},
VOLUME = {10},
YEAR = {2020},
NUMBER = {8},
ARTICLE-NUMBER = {518},
URL = {https://www.mdpi.com/2075-4418/10/8/518},
PubMedID = {32722605},
ISSN = {2075-4418},
ABSTRACT = {The purpose of this research was to provide a &ldquo;systematic literature review&rdquo; of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches&mdash;to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.},
DOI = {10.3390/diagnostics10080518}
}
@article {Merchan2022,
      author = "E Carlos Rodríguez-Merchán",
      title = "The current role of the virtual elements of artificial intelligence in total knee arthroplasty",
      journal = "EFORT Open Reviews",
      year = "2022",
      publisher = "Bioscientifica Ltd",
      address = "Bristol, UK",
      volume = "7",
      number = "7",
      doi = "10.1530/EOR-21-0107",
      pages=      "491 - 497",
      url = "https://eor.bioscientifica.com/view/journals/eor/7/7/EOR-21-0107.xml"
}
@article{Schmidt2022,
  title     = "Generalizability of deep learning segmentation algorithms for
               automated assessment of cartilage morphology and {MRI}
               relaxometry",
  author    = "Schmidt, Andrew M and Desai, Arjun D and Watkins, Lauren E and
               Crowder, Hollis A and Black, Marianne S and Mazzoli, Valentina
               and Rubin, Elka B and Lu, Quin and MacKay, James W and Boutin,
               Robert D and Kogan, Feliks and Gold, Garry E and Hargreaves,
               Brian A and Chaudhari, Akshay S",
  abstract  = "BACKGROUND: Deep learning (DL)-based automatic segmentation
               models can expedite manual segmentation yet require
               resource-intensive fine-tuning before deployment on new
               datasets. The generalizability of DL methods to new datasets
               without fine-tuning is not well characterized. PURPOSE: Evaluate
               the generalizability of DL-based models by deploying pretrained
               models on independent datasets varying by MR scanner,
               acquisition parameters, and subject population. STUDY TYPE:
               Retrospective based on prospectively acquired data. POPULATION:
               Overall test dataset: 59 subjects (26 females); Study 1: 5
               healthy subjects (zero females), Study 2: 8 healthy subjects
               (eight females), Study 3: 10 subjects with osteoarthritis (eight
               females), Study 4: 36 subjects with various knee pathology (10
               females). FIELD STRENGTH/SEQUENCE: A 3-T, quantitative
               double-echo steady state (qDESS). ASSESSMENT: Four annotators
               manually segmented knee cartilage. Each reader segmented one of
               four qDESS datasets in the test dataset. Two DL models, one
               trained on qDESS data and another on Osteoarthritis Initiative
               (OAI)-DESS data, were assessed. Manual and automatic
               segmentations were compared by quantifying variations in
               segmentation accuracy, volume, and T2 relaxation times for
               superficial and deep cartilage. STATISTICAL TESTS: Dice
               similarity coefficient (DSC) for segmentation accuracy. Lin's
               concordance correlation coefficient (CCC), Wilcoxon rank-sum
               tests, root-mean-squared error-coefficient-of-variation to
               quantify manual vs. automatic T2 and volume variations.
               Bland-Altman plots for manual vs. automatic T2 agreement. A P
               value < 0.05 was considered statistically significant. RESULTS:
               DSCs for the qDESS-trained model, 0.79-0.93, were higher than
               those for the OAI-DESS-trained model, 0.59-0.79. T2 and volume
               CCCs for the qDESS-trained model, 0.75-0.98 and 0.47-0.95, were
               higher than respective CCCs for the OAI-DESS-trained model,
               0.35-0.90 and 0.13-0.84. Bland-Altman 95\% limits of agreement
               for superficial and deep cartilage T2 were lower for the
               qDESS-trained model, $\pm$2.4 msec and $\pm$4.0 msec, than the
               OAI-DESS-trained model, $\pm$4.4 msec and $\pm$5.2 msec. DATA
               CONCLUSION: The qDESS-trained model may generalize well to
               independent qDESS datasets regardless of MR scanner, acquisition
               parameters, and subject population. EVIDENCE LEVEL: 1 TECHNICAL
               EFFICACY: Stage 1.",
  journal   = "J. Magn. Reson. Imaging",
  publisher = "Wiley",
  month     =  jul,
  year      =  2022,
  keywords  = "cartilage; machine learning; osteoarthritis; qMRI; segmentation",
  copyright = "http://onlinelibrary.wiley.com/termsAndConditions\#vor",
  language  = "en"
}
@article{Yang2022,
	author = {Mingrui Yang and Ceylan Colak and Kishore K. Chundru and Sibaji Gaj and Andreas Nanavati and Morgan H. Jones and Carl S. Winalski and Naveen Subhas and Xiaojuan Li},
	title = {Automated knee cartilage segmentation for heterogeneous clinical MRI using generative adversarial networks with transfer learning},
	journal = {Quantitative Imaging in Medicine and Surgery},
	volume = {12},
	number = {5},
	year = {2022},
	keywords = {},
	abstract = {},
	issn = {2223-4306},	url = {https://qims.amegroups.com/article/view/89910}
}
@article{Chadoulos2022,
title = {A novel multi-atlas segmentation approach under the semi-supervised learning framework: Application to knee cartilage segmentation},
journal = {Computer Methods and Programs in Biomedicine},
volume = {227},
pages = {107208},
year = {2022},
issn = {0169-2607},
doi = {https://doi.org/10.1016/j.cmpb.2022.107208},
url = {https://www.sciencedirect.com/science/article/pii/S0169260722005892},
author = {Christos G. Chadoulos and Dimitrios E. Tsaopoulos and Serafeim Moustakidis and Nikolaos L. Tsakiridis and John B. Theocharis},
keywords = {Osteoarthritis (OA), Magnetic resonance imaging (MRI), Knee cartilage segmentation, Multi-Atlas patch-Based, Semi-Supervised learning (), Sparse coding (),},
abstract = {Background and objective: Multi-atlas based segmentation techniques, which rely on an atlas library comprised of training images labeled by an expert, have proven their effectiveness in multiple automatic segmentation applications. However, the usage of exhaustive patch libraries combined with the voxel-wise labeling incur a large computational cost in terms of memory requirements and execution times. Methods: To confront this shortcoming, we propose a novel two-stage multi-atlas approach designed under the Semi-Supervised Learning (SSL) framework. The main properties of our method are as follows: First, instead of the voxel-wise labeling approach, the labeling of target voxels is accomplished here by exploiting the spectral content of globally sampled datasets from the target image, along with their spatially correspondent data collected from the atlases. Following SSL, voxels classification is boosted by incorporating unlabeled data from the target image, in addition to the labeled ones from atlas library. Our scheme integrates constructively fruitful concepts, including sparse reconstructions of voxels from linear neighborhoods, HOG feature descriptors of patches/regions, and label propagation via sparse graph constructions. Segmentation of the target image is carried out in two stages: stage-1 focuses on the sampling and labeling of global data, while stage-2 undertakes the above tasks for the out-of-sample data. Finally, we propose different graph-based methods for the labeling of global data, while these methods are extended to deal with the out-of-sample voxels. Results: A thorough experimental investigation is conducted on 76 subjects provided by the publicly accessible Osteoarthritis Initiative (OAI) repository. Comparative results and statistical analysis demonstrate that the suggested methodology exhibits superior segmentation performance compared to the existing patch-based methods, across all evaluation metrics (DSC:88.89\%, Precision: 89.86\%, Recall: 88.12\%), while at the same time it requires a considerably reduced computational load (>70\% reduction on average execution time with respect to other patch-based). In addition, our approach is favorably compared against other non patch-based and deep learning methods in terms of performance accuracy (on the 3-class problem). A final experimentation on a 5-class setting of the problems demonstrates that our approach is capable of achieving performance comparable to existing state-of-the-art knee cartilage segmentation methods (DSC:88.22\% and DSC:85.84\% for femoral and tibial cartilage respectively).}
}
@INPROCEEDINGS{Marsilio2022,
  title           = "{CEL-Unet}: a novel {CNN} architecture for {3D}
                     Segmentation of Knee Bones affected by Severe
                     Osteoarthritis for {PSI-Based} Surgical Planning",
  booktitle       = "2022 44th Annual International Conference of the {IEEE}
                     Engineering in Medicine \& Biology Society ({EMBC})",
  author          = "Marsilio, Luca and Faglia, Alberto and Rossi, Matteo and
                     Mainardi, Luca and Manzotti, Alfonso and Cerveri, Pietro",
  publisher       = "IEEE",
  month           =  jul,
  year            =  2022,
  conference      = "2022 44th Annual International Conference of the IEEE
                     Engineering in Medicine \& Biology Society (EMBC)",
  location        = "Glasgow, Scotland, United Kingdom"
}
@article{Felfeliya2022,
title = {Improved-Mask R-CNN: Towards an accurate generic MSK MRI instance segmentation platform (data from the Osteoarthritis Initiative)},
journal = {Computerized Medical Imaging and Graphics},
volume = {97},
pages = {102056},
year = {2022},
issn = {0895-6111},
doi = {https://doi.org/10.1016/j.compmedimag.2022.102056},
url = {https://www.sciencedirect.com/science/article/pii/S0895611122000295},
author = {Banafshe Felfeliyan and Abhilash Hareendranathan and Gregor Kuntze and Jacob L. Jaremko and Janet L. Ronsky},
keywords = {Deep learning, Tissue segmentation, Osteoarthritis, MRI, Bone and cartilage, Effusion},
abstract = {Introduction
Objective assessment of osteoarthritis (OA) Magnetic Resonance Imaging (MRI) scans can address the limitations of the current OA assessment approaches. Detecting and extracting bone, cartilage, and joint fluid is a necessary component for the objective assessment of OA, which helps to quantify tissue characteristics such as volume and thickness. Many algorithms, based on Artificial Intelligence (AI), have been proposed over recent years for segmenting bone and soft tissues. Most of these segmentation methods suffer from the class imbalance problem, can't differentiate between the same anatomic structure, or do not support segmenting different rang of tissue sizes. Mask R-CNN is an instance segmentation framework, meaning it segments and distinct each object of interest like different anatomical structures (e.g. bone and cartilage) using a single model. In this study, the Mask R-CNN architecture was deployed to address the need for a segmentation method that is applicable to use for different tissue scales, pathologies, and MRI sequences associated with OA, without having a problem with imbalanced classes. In addition, we modified the Mask R-CNN to improve segmentation accuracy around instance edges.
Methods
A total of 500 adult knee MRI scans from the publicly available Osteoarthritis Initiative (OAI), and 97 hip MRI scans from adults with symptomatic hip OA, evaluated by two readers, were used for training and validating the network. Three specific modifications to Mask R-CNN yielded the improved-Mask R-CNN (iMaskRCNN): an additional ROIAligned block, an extra decoder block in the segmentation header, and connecting them using a skip connection. The results were evaluated using Hausdorff distance, dice score for bone and cartilage segmentation, and differences in detected volume, dice score, and coefficients of variation (CoV) for effusion segmentation.
Conclusion
Mask R-CNN and iMaskRCNN can reliably and simultaneously extract different scale articular tissues involved in OA, forming the foundation for automated assessment of OA. The iMaskRCNN results show that the modification improved the network performance around the edges.}
}
@article{Panfilov2022_Segmentation,
  title     = "Deep learning-based segmentation of knee {MRI} for fully
               automatic subregional morphological assessment of cartilage
               tissues: Data from the Osteoarthritis Initiative",
  author    = "Panfilov, Egor and Tiulpin, Aleksei and Nieminen, Miika T and
               Saarakkala, Simo and Casula, Victor",
  abstract  = "Morphological changes in knee cartilage subregions are valuable
               imaging-based biomarkers for understanding progression of
               osteoarthritis, and they are typically detected from magnetic
               resonance imaging (MRI). So far, accurate segmentation of
               cartilage has been done manually. Deep learning approaches show
               high promise in automating the task; however, they lack
               clinically relevant evaluation. We introduce a fully automatic
               method for segmentation and subregional assessment of articular
               cartilage, and evaluate its predictive power in context of
               radiographic osteoarthritis progression. Two data sets of 3D
               double-echo steady-state (DESS) MRI derived from the
               Osteoarthritis Initiative were used: first, n = 88; second, n =
               600, 0-/12-/24-month visits. Our method performed deep
               learning-based segmentation of knee cartilage tissues, their
               subregional division via multi-atlas registration, and
               extraction of subregional volume and thickness. The segmentation
               model was developed and assessed on the first data set.
               Subsequently, on the second data set, the morphological
               measurements from our and the prior methods were analyzed in
               correlation and agreement, and, eventually, by their
               discriminative power of radiographic osteoarthritis progression
               over 12 and 24 months, retrospectively. The segmentation model
               showed very high correlation (r > 0.934) and agreement (mean
               difference < 116 mm3 ) in volumetric measurements with the
               reference segmentations. Comparison of our and manual
               segmentation methods yielded r = 0.845-0.973 and mean
               differences = 262-501 mm3 for weight-bearing cartilage volume,
               and r = 0.770-0.962 and mean differences = 0.513-1.138 mm for
               subregional cartilage thickness. With regard to osteoarthritis
               progression, our method found most of the significant
               associations identified using the manual segmentation method,
               for both 12- and 24-month subregional cartilage changes. The
               method may be effectively applied in osteoarthritis progression
               studies to extract cartilage-related imaging biomarkers.",
  journal   = "J. Orthop. Res.",
  publisher = "Wiley",
  volume    =  40,
  number    =  5,
  pages     = "1113--1124",
  month     =  may,
  year      =  2022,
  keywords  = "cartilage; deep learning; morphology; osteoarthritis;
               segmentation",
  copyright = "http://creativecommons.org/licenses/by/4.0/",
  language  = "en"
}

@article{Zhou2018,
  title     = "Deep convolutional neural network for segmentation of knee joint
               anatomy",
  author    = "Zhou, Zhaoye and Zhao, Gengyan and Kijowski, Richard and Liu,
               Fang",
  abstract  = "PURPOSE: To describe and evaluate a new segmentation method
               using deep convolutional neural network (CNN), 3D fully
               connected conditional random field (CRF), and 3D simplex
               deformable modeling to improve the efficiency and accuracy of
               knee joint tissue segmentation. METHODS: A segmentation pipeline
               was built by combining a semantic segmentation CNN, 3D fully
               connected CRF, and 3D simplex deformable modeling. A
               convolutional encoder-decoder network was designed as the core
               of the segmentation method to perform high resolution pixel-wise
               multi-class tissue classification for 12 different joint
               structures. The 3D fully connected CRF was applied to regularize
               contextual relationship among voxels within the same tissue
               class and between different classes. The 3D simplex deformable
               modeling refined the output from 3D CRF to preserve the overall
               shape and maintain a desirable smooth surface for joint
               structures. The method was evaluated on 3D fast spin-echo
               (3D-FSE) MR image data sets. Quantitative morphological metrics
               were used to evaluate the accuracy and robustness of the method
               in comparison to the ground truth data. RESULTS: The proposed
               segmentation method provided good performance for segmenting all
               knee joint structures. There were 4 tissue types with high mean
               Dice coefficient above 0.9 including the femur, tibia, muscle,
               and other non-specified tissues. There were 7 tissue types with
               mean Dice coefficient between 0.8 and 0.9 including the femoral
               cartilage, tibial cartilage, patella, patellar cartilage,
               meniscus, quadriceps and patellar tendon, and infrapatellar fat
               pad. There was 1 tissue type with mean Dice coefficient between
               0.7 and 0.8 for joint effusion and Baker's cyst. Most
               musculoskeletal tissues had a mean value of average symmetric
               surface distance below 1 mm. CONCLUSION: The combined CNN, 3D
               fully connected CRF, and 3D deformable modeling approach was
               well-suited for performing rapid and accurate comprehensive
               tissue segmentation of the knee joint. The deep learning-based
               segmentation method has promising potential applications in
               musculoskeletal imaging.",
  journal   = "Magn. Reson. Med.",
  publisher = "Wiley",
  volume    =  80,
  number    =  6,
  pages     = "2759--2770",
  month     =  dec,
  year      =  2018,
  keywords  = "conditional random field; deep learning; deformable model; image
               segmentation; knee; musculoskeletal imaging",
  copyright = "http://onlinelibrary.wiley.com/termsAndConditions\#vor",
  language  = "en"
}
@article{Peng2022,
title = {KCB-Net: A 3D knee cartilage and bone segmentation network via sparse annotation},
journal = {Medical Image Analysis},
volume = {82},
pages = {102574},
year = {2022},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2022.102574},
url = {https://www.sciencedirect.com/science/article/pii/S1361841522002146},
author = {Yaopeng Peng and Hao Zheng and Peixian Liang and Lichun Zhang and Fahim Zaman and Xiaodong Wu and Milan Sonka and Danny Z. Chen},
keywords = {Knee cartilage and bone segmentation, Sparse annotation, Ensemble learning, 3D MR images},
abstract = {Knee cartilage and bone segmentation is critical for physicians to analyze and diagnose articular damage and knee osteoarthritis (OA). Deep learning (DL) methods for medical image segmentation have largely outperformed traditional methods, but they often need large amounts of annotated data for model training, which is very costly and time-consuming for medical experts, especially on 3D images. In this paper, we report a new knee cartilage and bone segmentation framework, KCB-Net, for 3D MR images based on sparse annotation. KCB-Net selects a small subset of slices from 3D images for annotation, and seeks to bridge the performance gap between sparse annotation and full annotation. Specifically, it first identifies a subset of the most effective and representative slices with an unsupervised scheme; it then trains an ensemble model using the annotated slices; next, it self-trains the model using 3D images containing pseudo-labels generated by the ensemble method and improved by a bi-directional hierarchical earth mover’s distance (bi-HEMD) algorithm; finally, it fine-tunes the segmentation results using the primal–dual Internal Point Method (IPM). Experiments on four 3D MR knee joint datasets (the SKI10 dataset, OAI ZIB dataset, Iowa dataset, and iMorphics dataset) show that our new framework outperforms state-of-the-art methods on full annotation, and yields high quality results for small annotation ratios even as low as 10\%.}
}
@article{Khan2022,
title = {Deep collaborative network with alpha matte for precise knee tissue segmentation from MRI},
journal = {Computer Methods and Programs in Biomedicine},
volume = {222},
pages = {106963},
year = {2022},
issn = {0169-2607},
doi = {https://doi.org/10.1016/j.cmpb.2022.106963},
url = {https://www.sciencedirect.com/science/article/pii/S0169260722003455},
author = {Sheheryar Khan and Basim Azam and Yongcheng Yao and Weitian Chen},
keywords = {Knee MRI, Cartilage and meniscus segmentation, Low rank decomposition, Convolutional neural network, , Alpha matting},
abstract = {Background and objective
Precise segmentation of knee tissues from magnetic resonance imaging (MRI) is critical in quantitative imaging and diagnosis. Convolutional neural networks (CNNs), being state of the art, often challenged by the lack of image-specific adaptation, such as low tissue contrasts and structural inhomogeneities, thereby leading to incomplete segmentation results.
Methods
This paper presents a deep learning–based automatic segmentation framework for precise knee tissue segmentation. A novel deep collaborative method is proposed, which consists of an encoder–decoder-based segmentation network in combination with a low rank tensor-reconstructed segmentation network. Low rank reconstruction in MRI tensor sub-blocks is introduced to exploit the morphological variations in knee tissues. To model the tissue boundary regions and effectively utilize the superimposed regions, trimap generation is proposed for defining high, medium and low confidence regions from the multipath CNNs. The secondary path with low rank reconstructed input mitigates the conditions in which the primary segmentation network can potentially fail and overlook the boundary regions. The outcome of the segmentation is solved as an alpha matting problem by blending the trimap with the source input.
Results
Experiments on Osteoarthritis Initiative (OAI) datasets with all the 6 musculoskeletal tissues provide an overall segmentation dice score of 0.8925, where Femoral and Tibial part of cartilage achieving an average dice exceeding 0.9. The volumetric metrics also indicate the superior performances in all tissue compartments.
Conclusions
Experiments on Osteoarthritis Initiative (OAI) datasets and a self-prepared scan validate the effectiveness of the proposed method. Inclusion of extra prediction scale allowed the model to distinguish and segment the tissue boundary accurately. We specifically demonstrate the application of the proposed method in a cartilage segmentation–based thickness map for diagnosis purposes.}
}
@article{Li_Z2022,
author={Li, Zezhong
and Chen, Kangming
and Liu, Peng
and Chen, Xiaodong
and Zheng, Guoyan},
title={Entropy and distance maps-guided segmentation of articular cartilage: data from the Osteoarthritis Initiative},
journal={International Journal of Computer Assisted Radiology and Surgery},
year={2022},
month={Mar},
day={01},
volume={17},
number={3},
pages={553-560},
abstract={Accurate segmentation of articular cartilage from MR images is crucial for quantitative investigation of pathoanatomical conditions such as osteoarthritis (OA). Recently, deep learning-based methods have made significant progress in hard tissue segmentation. However, it remains a challenge to develop accurate methods for automatic segmentation of articular cartilage.},
issn={1861-6429},
doi={10.1007/s11548-021-02555-2},
url={https://doi.org/10.1007/s11548-021-02555-2}
}
@article{Latif2021,
title = {Automated tibiofemoral joint segmentation based on deeply supervised 2D-3D ensemble U-Net: Data from the Osteoarthritis Initiative},
journal = {Artificial Intelligence in Medicine},
volume = {122},
pages = {102213},
year = {2021},
issn = {0933-3657},
doi = {https://doi.org/10.1016/j.artmed.2021.102213},
url = {https://www.sciencedirect.com/science/article/pii/S0933365721002062},
author = {Muhamad Hafiz Abd Latif and Ibrahima Faye},
keywords = {Osteoarthritis, Tibiofemoral joint segmentation, CNN, Automated segmentation, Deep supervision, Deep learning},
}
@article{Gatti2021,
author={Gatti, Anthony A.
and Maly, Monica R.},
title={Automatic knee cartilage and bone segmentation using multi-stage convolutional neural networks: data from the osteoarthritis initiative},
journal={Magnetic Resonance Materials in Physics, Biology and Medicine},
year={2021},
month={Dec},
day={01},
volume={34},
number={6},
pages={859-875},
abstract={Accurate and efficient knee cartilage and bone segmentation are necessary for basic science, clinical trial, and clinical applications. This work tested a multi-stage convolutional neural network framework for MRI image segmentation.},
issn={1352-8661},
doi={10.1007/s10334-021-00934-z},
url={https://doi.org/10.1007/s10334-021-00934-z}
}
@INPROCEEDINGS{Pattanaik2022,  
author={Pattanaik, Priyadarshini Adyasha},  booktitle={2022 Trends in Electrical, Electronics, Computer Engineering Conference (TEECCON)},   title={Automated Segmentation for Knee Joint MRI Images Using Hybrid UNet+Attention},   year={2022},  volume={},  number={},  pages={56-61},  doi={10.1109/TEECCON54414.2022.9854515}
}
@article{Zaman2022, 
author = "Fahim Ahmed Zaman and Lichun Zhang and Honghai Zhang and Milan Sonka and Xiaodong Wu", title = "{Segmentation Quality Assessment by Automated Detection of Erroneous Surface Regions in Medical Images}", year = "2022", month = "5", url = "https://www.techrxiv.org/articles/preprint/Segmentation_Quality_Assessment_by_Automated_Detection_of_Erroneous_Surface_Regions_in_Medical_Images/19767661", doi = "10.36227/techrxiv.19767661.v1" 
} 
@article{Zhao_Jiang2022,
  title     = "Adaptive fusion of deep learning with statistical shape model
               for robust patella segmentation from {CT} images",
  author    = "Zhao, Jiachen and Jiang, Tianshu and Lin, Yi and Chan, Justin
               and Chan, Ping-Keung and Wen, Chunyi and Chen, Hao",
  journal   = "SSRN Electron. J.",
  publisher = "Elsevier BV",
  year      =  2022,
  language  = "en"
}

@article{Hirvasniemi2021_ky,
  title    = "A machine learning approach to distinguish between knees without
              and with osteoarthritis using {MRI-based} radiomic features from
              tibial bone",
  author   = "Hirvasniemi, Jukka and Klein, Stefan and Bierma-Zeinstra, Sita
              and Vernooij, Meike W and Schiphof, Dieuwke and Oei, Edwin H G",
  abstract = "OBJECTIVES: Our aim was to assess the ability of
              semi-automatically extracted magnetic resonance imaging
              (MRI)-based radiomic features from tibial subchondral bone to
              distinguish between knees without and with osteoarthritis.
              METHODS: The right knees of 665 females from the population-based
              Rotterdam Study scanned with 1.5T MRI were analyzed. A fast
              imaging employing steady-state acquisition sequence was used for
              the quantitative bone analyses. Tibial bone was segmented using a
              method that combines multi-atlas and appearance models. Radiomic
              features related to the shape and texture were calculated from
              six volumes of interests (VOIs) in the proximal tibia. Machine
              learning-based Elastic Net models with 10-fold cross-validation
              were used to distinguish between knees without and with MRI
              Osteoarthritis Knee Score (MOAKS)-based tibiofemoral
              osteoarthritis. Performance of the covariate (age and body mass
              index), image features, and combined covariate + image features
              models were assessed using the area under the receiver operating
              characteristic curve (ROC AUC). RESULTS: Of 665 analyzed knees,
              76 (11.4\%) had osteoarthritis. An ROC AUC of 0.68 (95\%
              confidence interval (CI): 0.60-0.75) was obtained using the
              covariate model. The image features model yielded an ROC AUC of
              0.80 (CI: 0.73-0.87). The model that combined image features from
              all VOIs and covariates yielded an ROC AUC of 0.80 (CI:
              0.73-0.87). CONCLUSION: Our results suggest that radiomic
              features are useful imaging biomarkers of subchondral bone for
              the diagnosis of osteoarthritis. An advantage of assessing bone
              on MRI instead of on radiographs is that other tissues can be
              assessed simultaneously. KEY POINTS: • Subchondral bone plays a
              role in the osteoarthritis disease processes. • MRI radiomics is
              a potential method for quantifying changes in subchondral bone. •
              Semi-automatically extracted radiomic features of tibia differ
              between subjects without and with osteoarthritis.",
  journal  = "Eur Radiol",
  volume   =  31,
  number   =  11,
  pages    = "8513--8521",
  month    =  apr,
  year     =  2021,
  address  = "Germany",
  keywords = "Bone; Knee osteoarthritis; Machine learning; Magnetic resonance
              imaging; Radiomics",
  language = "en"
}
@misc{Bayramoglu2021_2,
  doi = {10.48550/ARXIV.2106.01700},
  
  url = {https://arxiv.org/abs/2106.01700},
  
  author = {Bayramoglu, Neslihan and Nieminen, Miika T. and Saarakkala, Simo},
  
  keywords = {Image and Video Processing (eess.IV), Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Machine Learning Based Texture Analysis of Patella from X-Rays for Detecting Patellofemoral Osteoarthritis},
  
  publisher = {arXiv},
  
  year = {2021},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}
@misc{Panfilov2019,
  doi = {10.48550/ARXIV.1908.04126},
  url = {https://arxiv.org/abs/1908.04126},
  author = {Panfilov, Egor and Tiulpin, Aleksei and Klein, Stefan and Nieminen, Miika T. and Saarakkala, Simo},
  keywords = {Image and Video Processing (eess.IV), Computer Vision and Pattern Recognition (cs.CV), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation},
  publisher = {arXiv},
  year = {2019},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
@article{Desai2021_Challange,
  title     = "The international workshop on osteoarthritis Imaging knee {MRI}
               segmentation challenge: A multi-institute evaluation and
               analysis framework on a standardized dataset",
  author    = "Desai, Arjun D and Caliva, Francesco and Iriondo, Claudia and
               Mortazi, Aliasghar and Jambawalikar, Sachin and Bagci, Ulas and
               Perslev, Mathias and Igel, Christian and Dam, Erik B and Gaj,
               Sibaji and Yang, Mingrui and Li, Xiaojuan and Deniz, Cem M and
               Juras, Vladimir and Regatte, Ravinder and Gold, Garry E and
               Hargreaves, Brian A and Pedoia, Valentina and Chaudhari, Akshay
               S and {IWOAI Segmentation Challenge Writing Group}",
  abstract  = "PURPOSE: To organize a multi-institute knee MRI segmentation
               challenge for characterizing the semantic and clinical efficacy
               of automatic segmentation methods relevant for monitoring
               osteoarthritis progression. MATERIALS AND METHODS: A dataset
               partition consisting of three-dimensional knee MRI from 88
               retrospective patients at two time points (baseline and 1-year
               follow-up) with ground truth articular (femoral, tibial, and
               patellar) cartilage and meniscus segmentations was standardized.
               Challenge submissions and a majority-vote ensemble were
               evaluated against ground truth segmentations using Dice score,
               average symmetric surface distance, volumetric overlap error,
               and coefficient of variation on a holdout test set. Similarities
               in automated segmentations were measured using pairwise Dice
               coefficient correlations. Articular cartilage thickness was
               computed longitudinally and with scans. Correlation between
               thickness error and segmentation metrics was measured using the
               Pearson correlation coefficient. Two empirical upper bounds for
               ensemble performance were computed using combinations of model
               outputs that consolidated true positives and true negatives.
               RESULTS: Six teams (T 1-T 6) submitted entries for the
               challenge. No differences were observed across any segmentation
               metrics for any tissues (P = .99) among the four top-performing
               networks (T 2, T 3, T 4, T 6). Dice coefficient correlations
               between network pairs were high (> 0.85). Per-scan thickness
               errors were negligible among networks T 1-T 4 (P = .99), and
               longitudinal changes showed minimal bias (< 0.03 mm). Low
               correlations ($\rho$ < 0.41) were observed between segmentation
               metrics and thickness error. The majority-vote ensemble was
               comparable to top-performing networks (P = .99). Empirical
               upper-bound performances were similar for both combinations (P =
               .99). CONCLUSION: Diverse networks learned to segment the knee
               similarly, where high segmentation accuracy did not correlate
               with cartilage thickness accuracy and voting ensembles did not
               exceed individual network performance.See also the commentary by
               Elhalawani and Mak in this issue.Keywords: Cartilage, Knee,
               MR-Imaging, Segmentation \copyright{} RSNA, 2020Supplemental
               material is available for this article.",
  journal   = "Radiol. Artif. Intell.",
  publisher = "Radiological Society of North America (RSNA)",
  volume    =  3,
  number    =  3,
  pages     = "e200078",
  month     =  may,
  year      =  2021,
  language  = "en"
}
@inproceedings{Bayramoglu2020,
author="Bayramoglu, Neslihan
and Nieminen, Miika T.
and Saarakkala, Simo",
title="A Lightweight CNN and Joint Shape-Joint Space (JS2) Descriptor for Radiological Osteoarthritis Detection",
booktitle="Medical Image Understanding and Analysis",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="331--345",
abstract="Knee osteoarthritis (OA) is very common progressive and degenerative musculoskeletal disease worldwide creates a heavy burden on patients with reduced quality of life and also on society due to financial impact. Therefore, any attempt to reduce the burden of the disease could help both patients and society. In this study, we propose a fully automated novel method, based on combination of joint shape and convolutional neural network (CNN) based bone texture features, to distinguish between the knee radiographs with and without radiographic osteoarthritis. Moreover, we report the first attempt at describing the bone texture using CNN. Knee radiographs from Osteoarthritis Initiative (OAI) and Multicenter Osteoarthritis (MOST) studies were used in the experiments. Our models were trained on 8953 knee radiographs from OAI and evaluated on 3445 knee radiographs from MOST. Our results demonstrate that fusing the proposed shape and texture parameters achieves the state-of-the art performance in radiographic OA detection yielding area under the ROC curve (AUC) of {\$}{\$}95.21{\backslash}{\%}{\$}{\$}.",
isbn="978-3-030-52791-4"
}



@article{Norman2019,
  title     = "Applying densely connected convolutional neural networks for
               staging osteoarthritis severity from plain radiographs",
  author    = "Norman, Berk and Pedoia, Valentina and Noworolski, Adam and
               Link, Thomas M and Majumdar, Sharmila",
  abstract  = "Osteoarthritis (OA) classification in the knee is most commonly
               done with radiographs using the 0-4 Kellgren Lawrence (KL)
               grading system where 0 is normal, 1 shows doubtful signs of OA,
               2 is mild OA, 3 is moderate OA, and 4 is severe OA. KL grading
               is widely used for clinical assessment and diagnosis of OA,
               usually on a high volume of radiographs, making its automation
               highly relevant. We propose a fully automated algorithm for the
               detection of OA using KL gradings with a state-of-the-art neural
               network. Four thousand four hundred ninety bilateral PA
               fixed-flexion knee radiographs were collected from the
               Osteoarthritis Initiative dataset (age = 61.2 $\pm$ 9.2 years,
               BMI = 32.8 $\pm$ 15.9 kg/m2, 42/58 male/female split) for six
               different time points. The left and right knee joints were
               localized using a U-net model. These localized images were used
               to train an ensemble of DenseNet neural network architectures
               for the prediction of OA severity. This ensemble of DenseNets'
               testing sensitivity rates of no OA, mild, moderate, and severe
               OA were 83.7, 70.2, 68.9, and 86.0\% respectively. The
               corresponding specificity rates were 86.1, 83.8, 97.1, and
               99.1\%. Using saliency maps, we confirmed that the neural
               networks producing these results were in fact selecting the
               correct osteoarthritic features used in detection. These results
               suggest the use of our automatic classifier to assist
               radiologists in making more accurate and precise diagnosis with
               the increasing volume of radiographic image being taken in
               clinic.",
  journal   = "J. Digit. Imaging",
  publisher = "Springer Science and Business Media LLC",
  volume    =  32,
  number    =  3,
  pages     = "471--477",
  month     =  jun,
  year      =  2019,
  keywords  = "Machine learning; Neural networks; Osteoarthritis; Radiographs",
  language  = "en"
}
@article{Morales_Martinez2020,
  title     = "Learning osteoarthritis imaging biomarkers from bone surface
               spherical encoding",
  author    = "Morales Martinez, Alejandro and Caliva, Francesco and Flament,
               Io and Liu, Felix and Lee, Jinhee and Cao, Peng and Shah, Rutwik
               and Majumdar, Sharmila and Pedoia, Valentina",
  abstract  = "PURPOSE: To learn bone shape features from spherical bone map of
               knee MRI images using established convolutional neural networks
               (CNN) and use these features to diagnose and predict
               osteoarthritis (OA). METHODS: A bone segmentation model was
               trained on 25 manually annotated 3D MRI volumes to segment the
               femur, tibia, and patella from 47 078 3D MRI volumes. Each bone
               segmentation was converted to a 3D point cloud and transformed
               into spherical coordinates. Different fusion strategies were
               performed to merge spherical maps obtained by each bone. A total
               of 41 822 merged spherical maps with corresponding
               Kellgren-Lawrence grades for radiographic OA were used to train
               a CNN classifier model to diagnose OA using bone shape learned
               features. Several OA Diagnosis models were tested and the
               weights for each trained model were transferred to the OA
               Incidence models. The OA incidence task consisted of predicting
               OA from a healthy scan within a range of eight time points, from
               1 y to 8 y. The validation performance was compared and the test
               set performance was reported. RESULTS: The OA Diagnosis model
               had an area-under-the-curve (AUC) of 0.905 on the test set with
               a sensitivity and specificity of 0.815 and 0.839. The OA
               Incidence models had an AUC ranging from 0.841 to 0.646 on the
               test set for the range from 1 y to 8 y. CONCLUSION: Bone shape
               was successfully used as a predictive imaging biomarker for OA.
               This approach is novel in the field of deep learning
               applications for musculoskeletal imaging and can be expanded to
               other OA biomarkers.",
  journal   = "Magn. Reson. Med.",
  publisher = "Wiley",
  volume    =  84,
  number    =  4,
  pages     = "2190--2203",
  month     =  oct,
  year      =  2020,
  keywords  = "bone shape; deep learning; musculoskeletal MRI; osteoarthritis",
  copyright = "http://onlinelibrary.wiley.com/termsAndConditions\#vor",
  language  = "en"
}
@article{Dam2018,
author = {Dam, Erik B. and Runhaar, Jos and Bierma-Zienstra, Sita and Karsdal, Morten},
title = {Cartilage cavity—an MRI marker of cartilage lesions in knee OA with Data from CCBR, OAI, and PROOF},
journal = {Magnetic Resonance in Medicine},
volume = {80},
number = {3},
pages = {1219-1232},
keywords = {osteoarthritis, cartilage, integrity, biomarker},
doi = {https://doi.org/10.1002/mrm.27130},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.27130},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.27130},
abstract = {Purpose We introduce the quantitative, continuous marker cartilage cavity that quantifies cartilage lesions by the total lesion volume. The aim was to quantify small lesions as well as large, full-depth lesions. Methods We included 315 knees from the Center for Clinical and Basic Research (CCBR), 972 knees from the Osteoarthritis Initiative (OAI), and 791 knees from the Prevention of OA in Overweight Females (PROOF) cohorts. In a subset, we digitally inserted artificial lesions. Each knee MRI was segmented using the knee imaging quantification (KIQ) framework. We quantified cartilage mean thickness and cavity from high-resolution cartilage thickness maps. Finally, we quantified lesion volume by the gradient peak method (GPM). Results Scan–rescan precision for cartilage cavity was 7.1\%/3.0\%. The cartilage cavity accuracy on the artificial lesions was determined as linear correlation at 0.88 with an average 8\% under-estimation of lesion volume. Cavity and degree of radiographic osteoarthritis (ROA) correlated for all compartments (Spearman's rho between 0.14–0.56, P < 0.001). Cavity had modest correlations to whole-organ magnetic resonance imaging score (WORMS) cartilage lesion scores but strong correlations with Boston-Leeds osteoarthritis knee score (BLOKS)/MRI osteoarthritis knee score (MOAKS) scores in most compartments (rho between 0.08–0.65, P < 0.001). Cavity correlated with WOMAC pain for all tibio-femoral compartments in OAI (rho between 0.19–0.25, P < 0.001) and most compartments in PROOF. Comparing with the GPM estimate, cavity was more precise, more accurate, and correlated stronger with ROA, lesion scores, and pain levels. Conclusion The strong correlations with ROA, radiologist lesion scores, and pain demonstrated that cavity captured OA and lesion features. Thereby, it may be appropriate for quantification of cartilage surface irregularity.},
year = {2018}
}
@article{Tiulpin2022,
  title     = "Predicting total knee arthroplasty from ultrasonography using
               machine learning",
  author    = "Tiulpin, Aleksei and Saarakkala, Simo and Mathiessen, Alexander
               and Hammer, Hilde Berner and Furnes, Ove and Nordsletten, Lars
               and Englund, Martin and Magnusson, Karin",
  abstract  = "Objective: To investigate the value of ultrasonographic data in
               predicting total knee replacement (TKR). Design: Data from the
               Musculoskeletal Pain in Ullensaker study (MUST) was linked to
               the Norwegian Arthroplasty Register to form a 5-7 year
               prospective cohort study of 630 persons (69\% women, mean (SD)
               age 64 (8.7) years). We examined the predictive power of
               ultrasound (US) features, i.e. osteophytes, meniscal extrusion,
               synovitis in the suprapatellar recess, femoral cartilage
               thickness, and quality for future knee osteoarthritis (OA)
               surgery. We investigated 4 main settings for multivariate
               predictive modeling: 1) clinical predictors (age, sex, body mass
               index, knee injury, familial OA and workload), 2) radiographic
               data (assessed by the Kellgren Lawrence grade, KL) with clinical
               predictors, 3) US features and clinical predictors. Finally, we
               also considered an ensemble of models 2) and 3) and used it as
               our fifth model. All models were compared using the Average
               Precision (AP) and the Area Under Receiver Operating
               Characteristic Curve (AUC) metrics. Results: Clinical predictors
               yielded AP of 0.11 (95\% confidence interval [CI] 0.05-0.23) and
               AUC of 0.69 (0.58-0.79). Clinical predictors with KL grade
               yielded AP of 0.20 (0.12-0.33) and AUC of 0.81 (0.67-0.90). The
               clinical variables with ultrasound yielded AP of 0.17
               (0.08-0.30) and AUC of 0.79 (0.69-0.86). Conclusion:
               Ultrasonographic examination of the knee may provide added value
               to basic clinical and demographic descriptors when predicting
               TKR. While it does not achieve the same predictive performance
               as radiography, it can provide additional value to the
               radiographic examination.",
  journal   = "Osteoarthr. Cartil. Open",
  publisher = "Elsevier BV",
  volume    =  4,
  number    =  4,
  pages     = "100319",
  month     =  dec,
  year      =  2022,
  keywords  = "Machine learning; Multivariate predictive modeling; Total knee
               replacement; Ultrasonography",
  copyright = "http://creativecommons.org/licenses/by/4.0/",
  language  = "en"
}
@article{Recht2020,
  title     = "Using deep learning to accelerate knee {MRI} at 3 T: Results of
               an interchangeability study",
  author    = "Recht, Michael P and Zbontar, Jure and Sodickson, Daniel K and
               Knoll, Florian and Yakubova, Nafissa and Sriram, Anuroop and
               Murrell, Tullie and Defazio, Aaron and Rabbat, Michael and
               Rybak, Leon and Kline, Mitchell and Ciavarra, Gina and Alaia,
               Erin F and Samim, Mohammad and Walter, William R and Lin, Dana J
               and Lui, Yvonne W and Muckley, Matthew and Huang, Zhengnan and
               Johnson, Patricia and Stern, Ruben and Zitnick, C Lawrence",
  abstract  = "OBJECTIVE. Deep learning (DL) image reconstruction has the
               potential to disrupt the current state of MRI by significantly
               decreasing the time required for MRI examinations. Our goal was
               to use DL to accelerate MRI to allow a 5-minute comprehensive
               examination of the knee without compromising image quality or
               diagnostic accuracy. MATERIALS AND METHODS. A DL model for image
               reconstruction using a variational network was optimized. The
               model was trained using dedicated multisequence training, in
               which a single reconstruction model was trained with data from
               multiple sequences with different contrast and orientations.
               After training, data from 108 patients were retrospectively
               undersampled in a manner that would correspond with a net
               3.49-fold acceleration of fully sampled data acquisition and a
               1.88-fold acceleration compared with our standard twofold
               accelerated parallel acquisition. An interchangeability study
               was performed, in which the ability of six readers to detect
               internal derangement of the knee was compared for clinical and
               DL-accelerated images. RESULTS. We found a high degree of
               interchangeability between standard and DL-accelerated images.
               In particular, results showed that interchanging the sequences
               would produce discordant clinical opinions no more than 4\% of
               the time for any feature evaluated. Moreover, the accelerated
               sequence was judged by all six readers to have better quality
               than the clinical sequence. CONCLUSION. An optimized DL model
               allowed acceleration of knee images that performed
               interchangeably with standard images for detection of internal
               derangement of the knee. Importantly, readers preferred the
               quality of accelerated images to that of standard clinical
               images.",
  journal   = "AJR Am. J. Roentgenol.",
  publisher = "American Roentgen Ray Society",
  volume    =  215,
  number    =  6,
  pages     = "1421--1429",
  month     =  dec,
  year      =  2020,
  keywords  = "MRI; acceleration; deep learning; internal derangement; knee",
  language  = "en"
}
@article{Zibetti2020,
author={Zibetti, Marcelo V. W.
and Johnson, Patricia M.
and Sharafi, Azadeh
and Hammernik, Kerstin
and Knoll, Florian
and Regatte, Ravinder R.},
title={Rapid mono and biexponential 3D-T1$\rho$ mapping of knee cartilage using variational networks},
journal={Scientific Reports},
year={2020},
month={Nov},
day={05},
volume={10},
number={1},
pages={19144},
abstract={In this study we use undersampled MRI acquisition methods to obtain accelerated 3D mono and biexponential spin--lattice relaxation time in the rotating frame (T1$\rho$) mapping of knee cartilage, reducing the usual long scan time. We compare the accelerated T1$\rho$ maps obtained by deep learning-based variational network (VN) and compressed sensing (CS). Both methods were compared with spatial (S) and spatio-temporal (ST) filters. Complex-valued fitting was used for T1$\rho$ parameters estimation. We tested with seven in vivo and six synthetic datasets, with acceleration factors (AF) from 2 to 10. Median normalized absolute deviation (MNAD), analysis of variance (ANOVA), and coefficient of variation (CV) were used for analysis. The methods CS-ST, VN-S, and VN-ST performed well for accelerating monoexponential T1$\rho$ mapping, with MNAD around 5{\%} for AF{\thinspace}={\thinspace}2, which increases almost linearly with the AF to an MNAD of 13{\%} for AF{\thinspace}={\thinspace}8, with all methods. For biexponential mapping, the VN-ST was the best method starting with MNAD of 7.4{\%} for AF{\thinspace}={\thinspace}2 and reaching MNAD of 13.1{\%} for AF{\thinspace}={\thinspace}8. The VN was able to produce 3D-T1$\rho$ mapping of knee cartilage with lower error than CS. The best results were obtained by VN-ST, improving CS-ST method by nearly 7.5{\%}.},
issn={2045-2322},
doi={10.1038/s41598-020-76126-x},
url={https://doi.org/10.1038/s41598-020-76126-x}
}
@article{Liu2020,
  title    = "Improving quantitative magnetic resonance imaging using deep
              learning",
  author   = "Liu, Fang",
  abstract = "Deep learning methods have shown promising results for
              accelerating quantitative musculoskeletal (MSK) magnetic
              resonance imaging (MRI) for T2 and T1$\rho$ relaxometry. These
              methods have been shown to improve musculoskeletal tissue
              segmentation on parametric maps, allowing efficient and accurate
              T2 and T1$\rho$ relaxometry analysis for monitoring and
              predicting MSK diseases. Deep learning methods have shown
              promising results for disease detection on quantitative MRI with
              diagnostic performance superior to conventional machine-learning
              methods for identifying knee osteoarthritis.",
  journal  = "Semin. Musculoskelet. Radiol.",
  volume   =  24,
  number   =  4,
  pages    = "451--459",
  month    =  aug,
  year     =  2020,
  language = "en"
}

@article{Eijgenraam2020,
  title     = "Time-saving opportunities in knee osteoarthritis: {T2} mapping
               and structural imaging of the knee using a single 5-min {MRI}
               scan",
  author    = "Eijgenraam, Susanne M and Chaudhari, Akshay S and Reijman, Max
               and Bierma-Zeinstra, Sita M A and Hargreaves, Brian A and
               Runhaar, Jos and Heijboer, Frank W J and Gold, Garry E and Oei,
               Edwin H G",
  abstract  = "OBJECTIVES: To assess the discriminative power of a 5-min
               quantitative double-echo steady-state (qDESS) sequence for
               simultaneous T2 measurements of cartilage and meniscus, and
               structural knee osteoarthritis (OA) assessment, in a clinical OA
               population, using radiographic knee OA as reference standard.
               METHODS: Fifty-three subjects were included and divided over
               three groups based on radiographic and clinical knee OA: 20
               subjects with no OA (Kellgren-Lawrence grade (KLG) 0), 18 with
               mild OA (KLG2), and 15 with moderate OA (KLG3). All patients
               underwent a 5-min qDESS scan. We measured T2 relaxation times in
               four cartilage and four meniscus regions of interest (ROIs) and
               performed structural OA evaluation with the MRI Osteoarthritis
               Knee Score (MOAKS) using qDESS with multiplanar reformatting.
               Between-group differences in T2 values and MOAKS were calculated
               using ANOVA. Correlations of the reference standard (i.e.,
               radiographic knee OA) with T2 and MOAKS were assessed with
               correlation analyses for ordinal variables. RESULTS: In
               cartilage, mean T2 values were 36.1 $\pm$ SD 4.3, 40.6 $\pm$
               5.9, and 47.1 $\pm$ 4.3 ms for no, mild, and moderate OA,
               respectively (p < 0.001). In menisci, mean T2 values were 15
               $\pm$ 3.6, 17.5 $\pm$ 3.8, and 20.6 $\pm$ 4.7 ms for no, mild,
               and moderate OA, respectively (p < 0.001). Statistically
               significant correlations were found between radiographic OA and
               T2 and between radiographic OA and MOAKS in all ROIs (p < 0.05).
               CONCLUSION: Quantitative T2 and structural assessment of
               cartilage and meniscus, using a single 5-min qDESS scan, can
               distinguish between different grades of radiographic OA,
               demonstrating the potential of qDESS as an efficient tool for OA
               imaging. KEY POINTS: • Quantitative T2values of cartilage and
               meniscus as well as structural assessment of the knee with a
               single 5-min quantitative double-echo steady-state (qDESS) scan
               can distinguish between different grades of knee osteoarthritis
               (OA). • Quantitative and structural qDESS-based measurements
               correlate significantly with the reference standard,
               radiographic degree of OA, for all cartilage and meniscus
               regions. • By providing quantitative measurements and diagnostic
               image quality in one rapid MRI scan, qDESS has great potential
               for application in large-scale clinical trials in knee OA.",
  journal   = "Eur. Radiol.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  30,
  number    =  4,
  pages     = "2231--2240",
  month     =  apr,
  year      =  2020,
  keywords  = "Cartilage; Knee; Magnetic resonance imaging; Meniscus;
               Osteoarthritis",
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}
@article{DelGrande2021,
  title     = "Five-minute five-sequence knee {MRI} using combined simultaneous
               multislice and parallel imaging acceleration: Comparison with
               10-minute parallel imaging knee {MRI}",
  author    = "Del Grande, Filippo and Rashidi, Ali and Luna, Rodrigo and
               Delcogliano, Marco and Stern, Steven E and Dalili, Danoob and
               Fritz, Jan",
  abstract  = "Background Rapid knee MRI using combined simultaneous multislice
               (SMS) technique and parallel imaging (PI) acceleration can add
               value through reduced acquisition time but requires validation
               of clinical efficacy. Purpose To evaluate the performance of
               clinical fourfold SMS-PI-accelerated, 5-minute, five-sequence,
               multicontrast knee MRI protocols compared with standard twofold
               PI-accelerated, 10-minute knee MRI protocols. Materials and
               Methods Adults with painful knee conditions were prospectively
               enrolled from April 2018 to October 2019. Participants underwent
               fourfold SMS-PI-accelerated, 5-minute, turbo spin-echo (TSE)
               knee MRI and standard-of-care twofold PI-accelerated, 10-minute,
               TSE knee MRI at either 1.5 T or 3.0 T. Three radiologists
               independently evaluated the knee MRI studies for meniscal,
               tendinous, ligamentous, and osseocartilaginous injuries.
               Statistical analyses included k-based intermethod agreements and
               diagnostic performance testing. P 0.71 [95\% CI: 0.56, 0.83])
               and very good at 3.0 T ($\kappa$ >0.85 [95\% CI: 0.69, 0.96]).
               The diagnostic performances of corresponding 5-minute and
               10-minute MRI protocols were similar for 1.5 T, with areas under
               the receiver operating characteristic curve (AUCs) greater than
               0.78 (95\% CI: 0.71, 0.84) (P > .32), and 3.0 T, with AUCs
               greater than 0.83 (95\% CI: 0.78, 0.88) (P > .32). Conclusion
               Comparisons of 5-minute five-sequence simultaneous multislice-
               and parallel imaging (PI)-accelerated and 10-minute
               five-sequence PI-accelerated turbo spin-echo MRI of the knee
               suggest similar performances at 1.5 and 3.0 T. \copyright{}
               RSNA, 2021 Online supplemental material is available for this
               article. See also the editorial by Subhas in this issue.",
  journal   = "Radiology",
  publisher = "Radiological Society of North America (RSNA)",
  volume    =  299,
  number    =  3,
  pages     = "635--646",
  month     =  jun,
  year      =  2021,
  language  = "en"
}
@article{KimMW2022,
  title     = "Deep learning-enhanced parallel imaging and simultaneous
               multislice acceleration reconstruction in knee {MRI}",
  author    = "Kim, Minwoo and Lee, Sang-Min and Park, Chankue and Lee, Dongeon
               and Kim, Kang Soo and Jeong, Hee Seok and Kim, Shinyoung and
               Choi, Min-Hyeok and Nickel, Dominik",
  abstract  = "OBJECTIVES: This study aimed to examine various combinations of
               parallel imaging (PI) and simultaneous multislice (SMS)
               acceleration imaging using deep learning (DL)-enhanced and
               conventional reconstruction. The study also aimed at comparing
               the diagnostic performance of the various combinations in
               internal knee derangement and provided a quantitative evaluation
               of image sharpness and noise using edge rise distance (ERD) and
               noise power (NP), respectively. MATERIALS AND METHODS: The data
               from adult patients who underwent knee magnetic resonance
               imaging using various DL-enhanced acquisitions between June 2021
               and January 2022 were retrospectively analyzed. The participants
               underwent conventional 2-fold PI and DL protocols with 4- to
               8-fold acceleration imaging (P2S2 [2-fold PI with 2-fold SMS],
               P3S2, and P4S2). Three readers evaluated the internal knee
               derangement and the overall image quality. The diagnostic
               performance was calculated using consensus reading as a standard
               reference, and we conducted comparative evaluations. We
               calculated the ERD and NP for quantitative evaluations of image
               sharpness and noise, respectively. Interreader and intermethod
               agreements were calculated using Fleiss $\kappa$. RESULTS: A
               total of 33 patients (mean age, 49 $\pm$ 19 years; 20 women)
               were included in this study. The diagnostic performance for
               internal knee derangement and the overall image quality were
               similar among the evaluated protocols. The NP values were
               significantly lower using the DL protocols than with
               conventional imaging ( P 0.12). Interreader and intermethod
               agreements were moderate-to-excellent ($\kappa$ = 0.574-0.838)
               and good-to-excellent ($\kappa$ = 0.755-1.000), respectively. In
               addition, the mean acquisition time was reduced by 47\% when
               using DL with P2S2, by 62\% with P3S2, and by 71\% with P4S2,
               compared with conventional P2 imaging (2 minutes and 55
               seconds). CONCLUSIONS: The combined use of DL-enhanced 8-fold
               acceleration imaging (4-fold PI with 2-fold SMS) showed
               comparable performance with conventional 2-fold PI for the
               evaluation of internal knee derangement, with a 71\% reduction
               in acquisition time.",
  journal   = "Invest. Radiol.",
  publisher = "Ovid Technologies (Wolters Kluwer Health)",
  volume    =  57,
  number    =  12,
  pages     = "826--833",
  month     =  dec,
  year      =  2022,
  language  = "en"
}
@article{Tolpadi2022,
author={Tolpadi, Aniket A.
and Han, Misung
and Caliv{\`a}, Francesco
and Pedoia, Valentina
and Majumdar, Sharmila},
title={Region of interest-specific loss functions improve T2 quantification with ultrafast T2 mapping MRI sequences in knee, hip and lumbar spine},
journal={Scientific Reports},
year={2022},
month={Dec},
day={23},
volume={12},
number={1},
pages={22208},
abstract={MRI T2 mapping sequences quantitatively assess tissue health and depict early degenerative changes in musculoskeletal (MSK) tissues like cartilage and intervertebral discs (IVDs) but require long acquisition times. In MSK imaging, small features in cartilage and IVDs are crucial for diagnoses and must be preserved when reconstructing accelerated data. To these ends, we propose region of interest-specific postprocessing of accelerated acquisitions: a recurrent UNet deep learning architecture that provides T2 maps in knee cartilage, hip cartilage, and lumbar spine IVDs from accelerated T2-prepared snapshot gradient-echo acquisitions, optimizing for cartilage and IVD performance with a multi-component loss function that most heavily penalizes errors in those regions. Quantification errors in knee and hip cartilage were under 10{\%} and 9{\%} from acceleration factors R{\thinspace}={\thinspace}2 through 10, respectively, with bias for both under 3 ms for most of R{\thinspace}={\thinspace}2 through 12. In IVDs, mean quantification errors were under 12{\%} from R{\thinspace}={\thinspace}2 through 6. A Gray Level Co-Occurrence Matrix-based scheme showed knee and hip pipelines outperformed state-of-the-art models, retaining smooth textures for most R and sharper ones through moderate R. Our methodology yields robust T2 maps while offering new approaches for optimizing and evaluating reconstruction algorithms to facilitate better preservation of small, clinically relevant features.},
issn={2045-2322},
doi={10.1038/s41598-022-26266-z},
url={https://doi.org/10.1038/s41598-022-26266-z}
}
@article{Huang2022,
title = {DADP: Dynamic abnormality detection and progression for longitudinal knee magnetic resonance images from the Osteoarthritis Initiative},
journal = {Medical Image Analysis},
volume = {77},
pages = {102343},
year = {2022},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2021.102343},
url = {https://www.sciencedirect.com/science/article/pii/S1361841521003881},
author = {Chao Huang and Zhenlin Xu and Zhengyang Shen and Tianyou Luo and Tengfei Li and Daniel Nissman and Amanda Nelson and Yvonne Golightly and Marc Niethammer and Hongtu Zhu},
keywords = {Osteoarthritis, Abnormal region detection, Dynamic functional mixed effect model, Dynamic conditional random field model},
abstract = {Osteoarthritis (OA) is the most common disabling joint disease. Magnetic resonance (MR) imaging has been commonly used to assess knee joint degeneration due to its distinct advantage in detecting morphologic cartilage changes. Although several statistical methods over conventional radiography have been developed to perform quantitative cartilage analyses, little work has been done capturing the development and progression of cartilage lesions (or abnormal regions) and how they naturally progress. There are two major challenges, including (i) the lack of building spatial-temporal correspondences and correlations in cartilage thickness and (ii) the spatio-temporal heterogeneity in abnormal regions. The goal of this work is to propose a dynamic abnormality detection and progression (DADP) framework for quantitative cartilage analysis, while addressing the two challenges. First, spatial correspondences are established on flattened 2D cartilage thickness maps extracted from 3D knee MR images both across time within each subject and across all subjects. Second, a dynamic functional mixed effects model (DFMEM) is proposed to quantify abnormality progression across time points and subjects, while accounting for the spatio-temporal heterogeneity. We systematically evaluate our DADP using simulations and real data from the Osteoarthritis Initiative (OAI). Our results show that DADP not only effectively detects subject-specific dynamic abnormal regions, but also provides population-level statistical disease mapping and subgroup analysis.}
}
@article{Swagerty2001,
  title    = "Radiographic assessment of osteoarthritis",
  author   = "Swagerty, Jr, D L and Hellinger, D",
  abstract = "Osteoarthritis is one of the most prevalent and disabling chronic
              conditions affecting older adults and a significant public health
              problem among adults of working age. As the bulk of the U.S.
              population ages, the prevalence of osteoarthritis is expected to
              rise. Although the incidence of osteoarthritis increases with
              age, the condition is not a normal part of the aging process.
              More severe symptoms tend to occur in the radiographically more
              advanced stage of the disease; however, considerable discrepancy
              may exist between symptoms and the radiographic stage.
              Roentgenograms of involved joints may be useful in confirming the
              diagnosis of osteoarthritis, assessing the severity of the
              disease, reassuring the patient and excluding other pathologic
              conditions. The diagnosis of osteoarthritis is based primarily on
              the history and physical examination, but radiographic findings,
              including asymmetric joint space narrowing, subchondral
              sclerosis, osteophyte formation, subluxation and distribution
              patterns of osteoarthritic changes, can be helpful when the
              diagnosis is in question.",
  journal  = "Am. Fam. Physician",
  volume   =  64,
  number   =  2,
  pages    = "279--286",
  month    =  jul,
  year     =  2001,
  language = "en"
}
@article{Pelsma2022,
  title     = "Clinical and radiographic assessment of peripheral joints in
               controlled acromegaly",
  author    = "Pelsma, Iris C M and Kroon, Herman M and van Trigt, Victoria R
               and Pereira, Alberto M and Kloppenburg, Margreet and Biermasz,
               Nienke R and Claessen, Kim M J A",
  abstract  = "PURPOSE: Acromegalic arthropathy is a well-known phenomenon,
               occurring in most patients regardless of disease status. To
               date, solely hips, knees, hands, and spinal joints have been
               radiographically assessed. Therefore, this study aimed to assess
               the prevalence of joint symptoms and radiographic osteoarthritis
               (OA) of new, and established peripheral joint sites in
               well-controlled acromegaly. METHODS: Fifty-one acromegaly
               patients (56\% female, mean age 64 $\pm$ 12 years) in long-term
               remission for 18.3 years (median, IQR 7.2-25.4) were included.
               Nineteen patients currently received pharmacological treatment.
               Self-reported joint complaints were assessed using standardized
               interviews. Self-reported disability of the upper and lower
               limbs, and health-related quality of life (HR-QoL) were
               evaluated using validated questionnaires. Radiographic OA
               [defined as Kellgren \& Lawrence (KL) $\geq$ 2] was scored using
               (modified) KL methods. RESULTS: Radiographic signs of OA were
               present in 46 patients (90.2\%) with $\geq$ 2 joints affected in
               virtually all of these patients (N = 44; 95.7\%). Radiographic
               MTP1 OA was as prevalent as radiographic knee OA (N = 26,
               51.0\%), and radiographic glenohumeral OA was similarly
               prevalent as hip OA [N = 21 (41.2\%) vs. N = 24 (47.1\%)]. Risk
               factors for radiographic glenohumeral OA were higher
               pre-treatment IGF-1 levels [OR 1.06 (1.01-1.12), P = 0.021], and
               current pharmacological treatment [OR 5.01 (1.03-24.54), P =
               0.047], whereas no risk factors for MTP1 joint OA could be
               identified. CONCLUSION: Similar to previously-assessed
               peripheral joints, clinical and radiographic arthropathy of the
               shoulder and feet were prevalent in controlled acromegaly.
               Further studies on adequate management strategies of acromegalic
               arthropathy are needed.",
  journal   = "Pituitary",
  publisher = "Springer Science and Business Media LLC",
  volume    =  25,
  number    =  4,
  pages     = "622--635",
  month     =  aug,
  year      =  2022,
  keywords  = "Acromegaly; Arthropathy; Growth hormone; Insulin-like growth
               factor-1; Osteoarthritis; Shoulder",
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}
@article{Steenkamp2022,
  title     = "The correlation between clinical and radiological severity of
               osteoarthritis of the knee",
  author    = "Steenkamp, Wynand and Rachuene, Pududu Archie and Dey, Roopam
               and Mzayiya, Nkosiphendule Lindani and Ramasuvha, Brian Emmanuel",
  abstract  = "INTRODUCTION: Primary osteoarthritis (OA) is a common cause of
               knee pain. Appropriate management of knee OA is based on
               clinical and radiological findings. Pain, deformity, and
               functional impairments are major clinical factors considered
               along with radiological findings when making management
               decisions. Differences in management strategies might exist due
               to clinical and radiological factors. This study aims at finding
               possible associations between clinical and radiological
               observations. METHODS: A prospective cross-sectional study of 52
               patients with primary osteoarthritis of the knee managed
               conservatively at a tertiary hospital arthroplasty clinic was
               conducted for three months. English speaking patients with
               primary OA were identified and included in this study. Pain and
               functional impairment were assessed using Wong-Baker Faces pain
               scale, The Knee Society Score (KSS), and Western Ontario and
               McMaster Osteoarthritis Index (WOMAC). The Body Mass Index (BMI)
               of all participants was measured. Standard two views plain
               radiographs were used for radiographic grading of the OA.
               Anonymized radiographs were presented to two senior consultant
               orthopaedic surgeons who graded the OA using Kellgren and
               Lawrence (KL) and Ahlb{\"a}ck classification systems. The
               severity of the functional impairment and pain score was then
               compared to the radiological grading. RESULTS: The average age
               of our participants was 63 $\pm$ 9 years. Their average BMI was
               34.9 $\pm$ 8.4 kg/m2, median self-reported pain, total WOMAC,
               and pain WOMAC scores were 8, 60, and 13, respectively. We
               observed no significant correlation between BMI and pain scores.
               Inter-rater reliability for KL and Ahlb{\"a}ck grading was
               strong. There was no significant correlation between WOMAC
               scores and the radiological grades. CONCLUSION: There was no
               correlation between pain and functional scores, patient factors
               and radiological severity of OA of the knee.",
  journal   = "SICOT J.",
  publisher = "EDP Sciences",
  volume    =  8,
  pages     = "14",
  month     =  apr,
  year      =  2022,
  keywords  = "Functional impairment; Knee; Osteoarthritis; Radiologic grading",
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}
@article{Aghdam2022,
  title     = "Accuracy of magnetic resonance imaging in assessing knee
               cartilage changes over time in patients with osteoarthritis: A
               systematic review",
  author    = "Aghdam, Yashar Hashemi and Moradi, Amin and Gro{\ss}terlinden,
               Lars Gerhard and Jafari, Morteza Seyed and Heverhagen, Johannes
               T and Daneshvar, Keivan",
  abstract  = "Magnetic resonance imaging (MRI) is a technique useful for the
               diagnosis of cartilage damage due to high sensitivity to
               identify subchondral bone abnormalities and full-thickness
               cartilage lesions. The lack of a study on knee cartilage changes
               over time in patients with osteoarthritis (OA) by MRI technique
               led us to investigate the accuracy of MRI in identifying knee
               cartilage changes over time in patients with OA in a systematic
               review. In the present systematic review, started from the
               beginning of 2020 in one of the University Hospitals in Iran,
               the databases of CINAHL, Ovid, Elsevier, Scopus, PubMed, Science
               Direct, and Web of Science were searched using the keywords MRI,
               OA, Cartilage Lesion, Imaging Techniques. A total of 169
               articles were retrieved in the initial search, and after
               reviewing the titles, abstracts, and full-texts, finally, seven
               were enrolled in the systematic review. Review of the selected
               papers showed that a total of 1091 subjects were studied, of
               which 355 were males. The results of all the studies, except
               one, indicated the high accuracy of MRI to identify knee
               cartilage changes over time. MRI technique can show cartilage
               changes with high accuracy in patients with knee OA over time.
               We proved the potential of MRI to identify articular cartilage
               injuries in patients with OA and its importance to the
               evaluation of articular cartilage lesions along with other
               available techniques.",
  journal   = "North. Clin. Istanb.",
  publisher = "Kare Publishing",
  volume    =  9,
  number    =  4,
  pages     = "414--418",
  month     =  sep,
  year      =  2022,
  keywords  = "Cartilage lesion; imaging techniques; knee cartilage; magnetic
               resonance imaging; osteoarthritis",
  language  = "en"
}

@article{Gill2022,
  title     = "Association between statins and progression of osteoarthritis
               features on magnetic resonance imaging in a predominantly
               pre-radiographic cohort: the Vancouver Longitudinal Study of
               Early Knee Osteoarthritis ({VALSEKO)}: a cohort study",
  author    = "Gill, Jagdeep and Sayre, Eric C and Guermazi, Ali and Nicolaou,
               Savvas and Cibere, Jolanda",
  abstract  = "BACKGROUND: To evaluate the effect of statin use on
               osteoarthritis (OA) incidence/progression using magnetic
               resonance imaging (MRI) in a population-based cohort with
               predominantly pre-radiographic knee OA. METHODS: A cohort aged
               40-79 years with knee pain was recruited using random population
               sampling and followed for 7 years. Baseline exclusions were
               inflammatory arthritis, recent knee surgery/injury, and
               inability to undergo MRI. At baseline, current statin use was
               ascertained. Baseline and follow-up MRIs were read
               semi-quantitatively for cartilage damage (grade 0-4, 0/1
               collapsed, 6 regions), osteophytes (grade 0-3, 8 regions), bone
               marrow lesions (BML) (grade 0-3, 6 regions) and effusion (grade
               0-3). The primary outcome was cartilage damage
               incidence/progression, while secondary outcomes were
               incidence/progression of osteophytes, BML, and effusion, each
               defined as an increase by $\geq$1 grade at any region. To ensure
               population representative samples, sample weights were used.
               Logistic regression was used to assess the association of statin
               use at baseline with incidence/progression of MRI outcomes.
               Analyses were adjusted for sex, age, BMI, and multiple
               comorbidities requiring statin therapy. RESULTS: Of 255
               participants evaluated at baseline, 122 completed the 7-year
               follow-up. Statin use was not significantly associated with
               progression of cartilage damage (OR 0.82; 95\% CI 0.17, 4.06),
               osteophytes (OR 3.48; 95\% CI 0.40, 30.31), BML (OR 0.61; 95\%
               CI 0.12, 3.02), or effusion (OR 2.38; 95\% CI 0.42, 13.63),
               after adjusting for confounders. CONCLUSION: In this
               population-based cohort of predominantly pre-radiographic knee
               OA, statins did not affect MRI incidence/progression of
               cartilage damage, BML, osteophytes or effusion. Therefore,
               statin use does not appear to affect people with
               pre-radiographic stages of knee OA.",
  journal   = "BMC Musculoskelet. Disord.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  23,
  number    =  1,
  pages     = "937",
  month     =  oct,
  year      =  2022,
  keywords  = "Cartilage; Diagnostic imaging; Magnetic resonance imaging;
               Osteoarthritis; Statins",
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}

@ARTICLE{Jerban2022,
  
AUTHOR={Jerban, Saeed and Oei, Edwin H. G. and Ding, Jianping},   
	 
TITLE={Editorial: Cartilage assessment using quantitative MRI},      
	
JOURNAL={Frontiers in Endocrinology},      
	
VOLUME={13},           
	
YEAR={2022},      
	  
URL={https://www.frontiersin.org/articles/10.3389/fendo.2022.1092354},       
	
DOI={10.3389/fendo.2022.1092354},      
	
ISSN={1664-2392}   
   
}
@article{Huang_B2022,
author={Huang, Bing
and Huang, Yun
and Ma, Xin
and Chen, Yuequn},
title={Intelligent Algorithm-Based Magnetic Resonance for Evaluating the Effect of Platelet-Rich Plasma in the Treatment of Intractable Pain of Knee Arthritis},
journal={Contrast Media {\&} Molecular Imaging},
year={2022},
month={May},
day={26},
publisher={Hindawi},
volume={2022},
pages={9223928},
abstract={The application of intelligent algorithms in the treatment of intractable pain of patients with platelet-rich plasma (PRP) knee osteoarthritis by magnetic resonance was investigated. The automatic diagnosis of magnetic resonance knee osteoarthritis was established with multiple intelligent algorithms, including gray projection algorithm, adaptive binarization algorithm, and active shape model (ASM). The difference between automatic magnetic resonance detection indexes of the patients with knee osteoarthritis and artificial measurement results was analyzed. The included patients received PRP treatment. Knee osteoarthritis MRI osteoarthritis knee scores (KOA MOAKS) and Western Ontario and McMaster Universities arthritis index (WOMAC) before and after treatment were compared. The results showed that the results of knee osteoarthritis scores, inferior angle of femur, superior angle of tibia, and tibiofemoral angle (TFA) by automatic magnetic resonance diagnostic model were entirely consistent with artificial detection results. After the treatment, the total scores of knee lateral area, interior area, central area, and patellar area were all remarkably lower than those before the treatment (<span class="inline{\_}break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="19.289pt" style="vertical-align:-0.6370001pt" id="M1" height="9.2729pt" version="1.1" viewBox="-0.0498162 -8.6359 19.289 9.2729"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-81" d="M600 480C600 590 528 650 384 650H143L137 622C222 614 225 607 210 531L130 127C113 41 106 36 23 28L17 0H294L300 28C204 36 195 42 212 127L243 284L314 263C327 263 339 263 352 264C465 271 600 337 600 480ZM508 481C508 351 402 304 329 304C289 304 265 311 250 317L295 559C302 594 310 606 323 611C335 616 350 619 367 619C455 619 508 573 508 481Z"/></g><g transform="matrix(.013,0,0,-0.013,11.658,0)"><path id="g117-91" d="M512 -3V55L134 254V256L512 456V514L75 281V230L512 -3Z"/></g></svg><span class="irelop"></span><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="21.918pt" style="vertical-align:-0.6370001pt" height="9.2729pt" version="1.1" viewBox="22.8711838 -8.6359 21.918 9.2729"><g transform="matrix(.013,0,0,-0.013,22.921,0)"><path id="g113-49" d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"/></g><g transform="matrix(.013,0,0,-0.013,29.161,0)"><path id="g113-47" d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z"/></g><g transform="matrix(.013,0,0,-0.013,32.125,0)"><use xlink:href="{\#}g113-49"/></g><g transform="matrix(.013,0,0,-0.013,38.365,0)"><path id="g113-54" d="M153 550H386L412 615L406 623H120L82 318C104 327 142 338 184 338C294 338 347 275 347 187C347 112 305 39 221 39C160 39 119 71 97 89C88 97 80 96 71 90C59 80 50 67 49 57C48 45 52 36 66 23C80 9 123 -12 169 -12C221 -11 288 15 342 59C403 109 431 165 431 225C431 308 366 395 238 395C212 395 165 379 127 364L153 550Z"/></g></svg>).</span></span> After the treatment, knee KOA MOAKS scores and WOMAC scores were both lower than those before the treatment (<span class="inline{\_}break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="19.289pt" style="vertical-align:-0.6370001pt" id="M2" height="9.2729pt" version="1.1" viewBox="-0.0498162 -8.6359 19.289 9.2729"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="{\#}g113-81"/></g><g transform="matrix(.013,0,0,-0.013,11.658,0)"><use xlink:href="{\#}g117-91"/></g></svg><span class="irelop"></span><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="21.918pt" style="vertical-align:-0.6370001pt" height="9.2729pt" version="1.1" viewBox="22.8711838 -8.6359 21.918 9.2729"><g transform="matrix(.013,0,0,-0.013,22.921,0)"><use xlink:href="{\#}g113-49"/></g><g transform="matrix(.013,0,0,-0.013,29.161,0)"><use xlink:href="{\#}g113-47"/></g><g transform="matrix(.013,0,0,-0.013,32.125,0)"><use xlink:href="{\#}g113-49"/></g><g transform="matrix(.013,0,0,-0.013,38.365,0)"><use xlink:href="{\#}g113-54"/></g></svg>).</span></span> Visual analogue scale (VAS) scores 1 week, 2 weeks, and 3 weeks after the treatment were decreased compared with those before the treatment (<span class="inline{\_}break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="19.289pt" style="vertical-align:-0.6370001pt" id="M3" height="9.2729pt" version="1.1" viewBox="-0.0498162 -8.6359 19.289 9.2729"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="{\#}g113-81"/></g><g transform="matrix(.013,0,0,-0.013,11.658,0)"><use xlink:href="{\#}g117-91"/></g></svg><span class="irelop"></span><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="21.918pt" style="vertical-align:-0.6370001pt" height="9.2729pt" version="1.1" viewBox="22.8711838 -8.6359 21.918 9.2729"><g transform="matrix(.013,0,0,-0.013,22.921,0)"><use xlink:href="{\#}g113-49"/></g><g transform="matrix(.013,0,0,-0.013,29.161,0)"><use xlink:href="{\#}g113-47"/></g><g transform="matrix(.013,0,0,-0.013,32.125,0)"><use xlink:href="{\#}g113-49"/></g><g transform="matrix(.013,0,0,-0.013,38.365,0)"><use xlink:href="{\#}g113-54"/></g></svg>).</span></span> Relevant studies indicated that intelligent algorithm-based automatic magnetic resonance diagnostic knee osteoarthritis model showed good utilization values, which could provide the reference and basis for the treatment of the patients with knee osteoarthritis.},
issn={1555-4309},
doi={10.1155/2022/9223928},
url={https://doi.org/10.1155/2022/9223928}
}
@article{Ishibashi2022,
  title     = "Effusion detected by ultrasonography and overweight may predict
               the risk of knee osteoarthritis in females with early knee
               osteoarthritis: a retrospective analysis of Iwaki cohort data",
  author    = "Ishibashi, Kyota and Sasaki, Eiji and Chiba, Daisuke and Oyama,
               Tetsushi and Ota, Seiya and Ishibashi, Hikaru and Yamamoto, Yuji
               and Tsuda, Eiichi and Sawada, Kaori and Jung, Songee and
               Ishibashi, Yasuyuki",
  abstract  = "BACKGROUND: Knee osteoarthritis (OA) has enormous medical and
               socioeconomic burdens, which early diagnosis and intervention
               can reduce. We investigated the influence of knee effusion on
               the progression of knee OA in patients with early knee OA.
               METHODS: A total of 404 participants without radiographic knee
               OA were assessed from a 3-year longitudinal analysis.
               Participants were classified into non-OA and early knee OA
               groups. The effusion area (mm2) was quantified using
               ultrasonography. Receiver operating characteristic and logistic
               regression analyses were performed. RESULTS: At the 3-year
               follow-up, 114 of 349 knees (32\%) had progressed from non-OA
               and 32 of 55 knees (58\%) had progressed from early knee OA to
               radiographic knee OA. Logistic regression analysis showed that
               female sex (odds ratio [OR] 3.36, 95\% confidence interval [CIs]
               2.98-5.42), early knee OA (OR 2.02, 95\% CI 1.08-3.75), body
               mass index (OR 1.11, 95\% CI 1.02-1.19), and effusion area (OR
               1.01, 95\% CI 1.01-1.02) were significantly correlated with knee
               OA progression. Women who were overweight (body mass index
               $\geq$ 25 kg/m2) with more severe effusion had a higher risk of
               OA progression (area under the curve = 0.691, OR = 6.00)
               compared to those not overweight (area under the curve = 0.568,
               OR = 1.91). CONCLUSION: Knee effusion may be an indicator of the
               progression of early-stage knee OA.",
  journal   = "BMC Musculoskelet. Disord.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  23,
  number    =  1,
  pages     = "1021",
  month     =  nov,
  year      =  2022,
  keywords  = "Early knee osteoarthritis; Effusion; Longitudinal analysis;
               Overweight; Ultrasonography",
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}

@article{Sawitzke2022,
    author = {Sawitzke, Allen D. and Jackson, Christopher G. and Carlson, Kimberly and Bizien, Marcel D. and Leiner, Mathew and Reda, Domenic J. and Sindowski, Tom and Hanrahan, Christopher and Spencer, Richard G. and Kwoh, C. Kent and Lee, Susan J. and Hose, Kalli and Robin, Lisa and Cain, Donna W. and Taylor, Meredith D. and Bangerter, Neal and Finco, Martha and Clegg, Daniel O.},
    title = "{Effect of Pulsed Low-Intensity Ultrasonography on Symptom Relief and Tibiofemoral Articular Cartilage Thickness Among Veterans Affairs Enrollees With Knee Osteoarthritis: A Randomized Clinical Trial}",
    journal = {JAMA Network Open},
    volume = {5},
    number = {3},
    pages = {e220632-e220632},
    year = {2022},
    month = {03},
    abstract = "{Osteoarthritis (OA) is a major cause of disability in the US, with no approved treatments to slow progression, but animal models suggest that pulsed low-intensity ultrasonography (PLIUS) may promote cartilage growth.To evaluate the efficacy of PLIUS in providing symptom reduction and }",
    issn = {2574-3805},
    doi = {10.1001/jamanetworkopen.2022.0632},
    url = {https://doi.org/10.1001/jamanetworkopen.2022.0632},
    eprint = {https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2789714/sawitzke\_2022\_oi\_220042\_1646151739.34097.pdf},
}
@article{Abraham2011,
author={Abraham, Ajay M.
and Goff, Iain
and Pearce, Mark S.
and Francis, Roger M.
and Birrell, Fraser},
title={Reliability and validity of ultrasound imaging of features of knee osteoarthritis in the community},
journal={BMC Musculoskeletal Disorders},
year={2011},
month={Apr},
day={06},
volume={12},
number={1},
pages={70},
abstract={Radiographs are the main outcome measure in epidemiological studies of osteoarthritis (OA). Ultrasound imaging has unique advantages in that it involves no ionising radiation, is easy to use and visualises soft tissue structures. Our objective was to measure the inter-rater reliability and validity of ultrasound imaging in the detection of features of knee OA.},
issn={1471-2474},
doi={10.1186/1471-2474-12-70},
url={https://doi.org/10.1186/1471-2474-12-70}
}
@article{LeCun2015,
author={LeCun, Yann
and Bengio, Yoshua
and Hinton, Geoffrey},
title={Deep learning},
journal={Nature},
year={2015},
month={May},
day={01},
volume={521},
number={7553},
pages={436-444},
abstract={Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.},
issn={1476-4687},
doi={10.1038/nature14539},
url={https://doi.org/10.1038/nature14539}
}

@article{Almhdie_Imjabbar_Review2023,
AUTHOR = {Almhdie-Imjabbar, Ahmad and Toumi, Hechmi and Lespessailles, Eric},
TITLE = {Radiographic Biomarkers for Knee Osteoarthritis: A Narrative Review},
JOURNAL = {Life},
VOLUME = {13},
YEAR = {2023},
NUMBER = {1},
ARTICLE-NUMBER = {237},
URL = {https://www.mdpi.com/2075-1729/13/1/237},
ISSN = {2075-1729},
ABSTRACT = {Conventional radiography remains the most widely available imaging modality in clinical practice in knee osteoarthritis. Recent research has been carried out to develop novel radiographic biomarkers to establish the diagnosis and to monitor the progression of the disease. The growing number of publications on this topic over time highlights the necessity of a renewed review. Herein, we propose a narrative review of a selection of original full-text articles describing human studies on radiographic imaging biomarkers used for the prediction of knee osteoarthritis-related outcomes. To achieve this, a PubMed database search was used. A total of 24 studies were obtained and then classified based on three outcomes: (1) prediction of radiographic knee osteoarthritis incidence, (2) knee osteoarthritis progression and (3) knee arthroplasty risk. Results showed that numerous studies have reported the relevance of joint space narrowing score, Kellgren&ndash;Lawrence score and trabecular bone texture features as potential bioimaging markers in the prediction of the three outcomes. Performance results of reviewed prediction models were presented in terms of the area under the receiver operating characteristic curves. However, fair and valid comparisons of the models&rsquo; performance were not possible due to the lack of a unique definition of each of the three outcomes.},
DOI = {10.3390/life13010237}
}
@article{Hinterwimmer2022,
author={Hinterwimmer, Florian
and Lazic, Igor
and Suren, Christian
and Hirschmann, Michael T.
and Pohlig, Florian
and Rueckert, Daniel
and Burgkart, Rainer
and von Eisenhart-Rothe, R{\"u}diger},
title={Machine learning in knee arthroplasty: specific data are key---a systematic review},
journal={Knee Surgery, Sports Traumatology, Arthroscopy},
year={2022},
month={Feb},
day={01},
volume={30},
number={2},
pages={376-388},
abstract={Artificial intelligence (AI) in healthcare is rapidly growing and offers novel options of data analysis. Machine learning (ML) represents a distinct application of AI, which is capable of generating predictions and has already been tested in different medical specialties with various approaches such as diagnostic applications, cost predictions or identification of risk factors. In orthopaedics, this technology has only recently been introduced and the literature on ML in knee arthroplasty is scarce. In this review, we aim to investigate which predictions are already feasible using ML models in knee arthroplasty to identify prerequisites for the effective use of this novel approach. For this reason, we conducted a systematic review of ML algorithms for outcome prediction in knee arthroplasty.},
issn={1433-7347},
doi={10.1007/s00167-021-06848-6},
url={https://doi.org/10.1007/s00167-021-06848-6}
}
@article{Zheng2022,
title={Multimodal Magnetic Resonance Imaging to diagnose knee osteoarthritis under artificial intelligence}, 
url={https://pubmed.ncbi.nlm.nih.gov/35785062/},
journal={Computational intelligence and neuroscience},
publisher={U.S. National Library of Medicine}, 
author={C;, Zheng Z;He R;Lin C;Huang}
} 
@article{Brejnebol2022,
title = {External validation of an artificial intelligence tool for radiographic knee osteoarthritis severity classification},
journal = {European Journal of Radiology},
volume = {150},
pages = {110249},
year = {2022},
issn = {0720-048X},
doi = {https://doi.org/10.1016/j.ejrad.2022.110249},
url = {https://www.sciencedirect.com/science/article/pii/S0720048X22000997},
author = {Mathias Willadsen Brejnebøl and Philip Hansen and Janus Uhd Nybing and Rikke Bachmann and Ulrik Ratjen and Ida Vibeke Hansen and Anders Lenskjold and Martin Axelsen and Michael Lundemann and Mikael Boesen},
keywords = {Artificial intelligence, External validation, Knee osteoarthritis, Inter-rater agreement, Conventional radiography},
abstract = {
Conclusions
The AI tool achieved the same good-to-excellent agreement with the radiology consultant consensus for radiographic knee osteoarthritis severity classification as the consultants did with each other.}
}
 @article{Tang2022,
 title={Fully automatic knee joint segmentation and quantitative analysis for osteoarthritis from Magnetic Resonance (MR) images using a deep learning model}, 
 url={https://pubmed.ncbi.nlm.nih.gov/35698440/}, 
 journal={Medical science monitor international medical journal of experimental and clinical research}, 
 publisher={U.S. National Library of Medicine}, 
 author={Tang X;Guo D;Liu A;Wu D;Liu J;Xu N;Qin Y;}
 } 

@article{Wang_Zi2022,
  author={Wang, Zi and Qian, Chen and Guo, Di and Sun, Hongwei and Li, Rushuai and Zhao, Bo and Qu, Xiaobo},
  journal={IEEE Transactions on Medical Imaging}, 
  title={One-Dimensional Deep Low-Rank and Sparse Network for Accelerated MRI}, 
  year={2023},
  volume={42},
  number={1},
  pages={79-90},
  doi={10.1109/TMI.2022.3203312}}


@article{Roemer2022,
  title={Structural tissue damage and 24-month progression of semi-quantitative MRI biomarkers of knee osteoarthritis in the IMI-APPROACH cohort},
  author={Roemer, Frank W. and Jansen, Mylène and Marijnissen, Anne C. A. and Guermazi, Ali and Heiss, Rafael and Maschek, Susanne and Lalande, Agnes and Blanco, Francisco J. and Berenbaum, Francis and van de Stadt, Lotte A. and Kloppenburg, Margreet and Haugen, Ida K. and Ladel, Christoph H. and Bacardit, Jaume and Wisser, Anna and Eckstein, Felix and Lafeber, Floris P. J. G. and Weinans, Harrie H. and Wirth, Wolfgang},
  journal={BMC Musculoskeletal Disorders},
  volume={23},
  number={1},
  pages={988},
  year={2022},
  publisher={Springer Nature},
  doi={10.1186/s12891-022-05926-1},
  url={https://doi.org/10.1186/s12891-022-05926-1}
}

@article{Chitnavis_2022,
    author = {Chitnavis, Jai and Maghsoudi, Daniel and Grewal, Harshvir},
    title = "{Bilaterally symmetrical ganglion and subchondral cysts of the knee: a case report}",
    journal = {Journal of Surgical Case Reports},
    volume = {2022},
    number = {3},
    year = {2022},
    month = {03},
    abstract = "{A 49-year-old patient, with a maternal history of arthritis, presented with an extra-capsular lump of the left knee. Following magnetic resonance imaging (MRI) scan and excision biopsy, the lump proved to be a benign ganglion cyst. The scan had also shown focal subchondral cyst formation and articular cartilage wear in the medial femoral condyle and patella, with no other abnormality. Four years later, she presented with identical symptoms and signs in the right knee. MRI scan confirmed a symmetrical pattern of ganglion and subchondral cyst location and size. Although both ganglion and subchondral cysts of the knee are common, their development is poorly understood and this combination of cyst formation and chondral wear has not been previously documented. Symmetry of end-stage knee osteoarthritis (OA) is recognized by clinicians. We speculate that OA of the knee begins asymptomatically with point-specific loss of articular cartilage, typically reflecting an inherent and bilateral vulnerability to degeneration.}",
    issn = {2042-8812},
    doi = {10.1093/jscr/rjac064},
    url = {https://doi.org/10.1093/jscr/rjac064},
    note = {rjac064},
    eprint = {https://academic.oup.com/jscr/article-pdf/2022/3/rjac064/42972621/rjac064.pdf},
}

@article{Oo_WinMin_2016,
  author = {Oo, Win Min and Bo, Myat Thae},
  year = {2016},
  title = {Role of Ultrasonography in Knee Osteoarthritis},
  journal = {JCR: Journal of Clinical Rheumatology},
  volume = {22},
  number = {6},
  url = {https://journals.lww.com/jclinrheum/Fulltext/2016/09000/Role_of_Ultrasonography_in_Knee_Osteoarthritis.7.aspx},
  abstract = {Ultrasound has become popular among rheumatologists as the first-choice imaging investigation for the evaluation and monitoring of osteoarthritis (OA). Because of recent improvement in technology, ultrasound has the ability to demonstrate and assess the minimal structural abnormalities, which involve the pathophysiology and progression of OA, such as articular cartilage, synovial tissue, bony cortex, and other soft tissue. Nowadays, ultrasonography is a promising technique for assessing soft tissue abnormalities such as joint effusion, synovial hypertrophy, Baker cyst, and other structural changes including the decrease in cartilage thickness, meniscus bulging, and formation of osteophyte. Ultrasonography not only possesses diagnostic potential in knee OA but also reveals long-term predictability for disease progress as imaging biomarker. Ultrasonography has also been proven as a useful tool in guiding therapeutic interventions and monitoring treatment effectiveness. This review addresses the utility, reliability, and potential utilization of ultrasonography as an imaging technique in knee OA.},
  issn = {1076-1608},
  keywords = {cartilage, knee osteoarthritis, musculoskeletal ultrasound, osteophytes, synovitis, ultrasonography}
}

@InProceedings{Qin_Liu2022C,
author="Liu, Qin
and Xu, Zhenlin
and Jiao, Yining
and Niethammer, Marc",
editor="Wang, Linwei
and Dou, Qi
and Fletcher, P. Thomas
and Speidel, Stefanie
and Li, Shuo",
title="iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022",
year="2022",
publisher="Springer Nature Switzerland",
address="Cham",
pages="464--474",
abstract="Interactive image segmentation has been widely applied to obtain high-quality voxel-level labels for medical images. The recent success of Transformers on various vision tasks has paved the road for developing Transformer-based interactive image segmentation approaches. However, these approaches remain unexplored and, in particular, have not been developed for 3D medical image segmentation. To fill this research gap, we investigate Transformer-based interactive image segmentation and its application to 3D medical images. This is a nontrivial task due to two main challenges: 1) limited memory for computationally inefficient Transformers and 2) limited labels for 3D medical images. To tackle the first challenge, we propose iSegFormer, a memory-efficient Transformer that combines a Swin Transformer with a lightweight multilayer perceptron (MLP) decoder. To address the second challenge, we pretrain iSegFormer on large amount of unlabeled datasets and then finetune it with only a limited number of segmented 2D slices. We further propagate the 2D segmentations obtained by iSegFormer to unsegmented slices in 3D images using a pre-existing segmentation propagation model pretrained on videos. We evaluate iSegFormer on the public OAI-ZIB dataset for interactive knee cartilage segmentation. Evaluation results show that iSegFormer outperforms its convolutional neural network (CNN) counterparts on interactive 2D knee cartilage segmentation, with competitive computational efficiency. When propagating the 2D interactive segmentations of 5 slices to other unprocessed slices within the same 3D volume, we achieve 82.2{\%} Dice score for 3D knee cartilage segmentation. Code is available at https://github.com/uncbiag/iSegFormer.",
isbn="978-3-031-16443-9"
}

@InProceedings{Liang_Dong2022C,
author="Liang, Dong
and Liu, Jun
and Wang, Kuanquan
and Luo, Gongning
and Wang, Wei
and Li, Shuo",
editor="Wang, Linwei
and Dou, Qi
and Fletcher, P. Thomas
and Speidel, Stefanie
and Li, Shuo",
title="Position-Prior Clustering-Based Self-attention Module for Knee Cartilage Segmentation",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022",
year="2022",
publisher="Springer Nature Switzerland",
address="Cham",
pages="193--202",
abstract="The morphological changes in knee cartilage (especially femoral and tibial cartilages) are closely related to the progression of knee osteoarthritis, which is expressed by magnetic resonance (MR) images and assessed on the cartilage segmentation results. Thus, it is necessary to propose an effective automatic cartilage segmentation model for longitudinal research on osteoarthritis. In this research, to relieve the problem of inaccurate discontinuous segmentation caused by the limited receptive field in convolutional neural networks, we proposed a novel position-prior clustering-based self-attention module (PCAM). In PCAM, long-range dependency between each class center and feature point is captured by self-attention allowing contextual information re-allocated to strengthen the relative features and ensure the continuity of segmentation result. The clutsering-based method is used to estimate class centers, which fosters intra-class consistency and further improves the accuracy of segmentation results. The position-prior excludes the false positives from side-output and makes center estimation more precise. Sufficient experiments are conducted on OAI-ZIB dataset. The experimental results show that the segmentation performance of combination of segmentation network and PCAM obtains an evident improvement compared to original model, which proves the potential application of PCAM in medical segmentation tasks. The source code is publicly available from link: https://github.com/PerceptionComputingLab/PCAMNet",
isbn="978-3-031-16443-9"
}

@inproceedings{Yang_Yanwu2022,
title={Regularizing Brain Age Prediction via Gated Knowledge Distillation},
author={Yanwu Yang and Guo Xutao and Chenfei Ye and Yang Xiang and Ting Ma},
booktitle={Medical Imaging with Deep Learning},
year={2022},
url={https://openreview.net/forum?id=nxA2bZff3iQ}
}

@misc{Balestriero2023cookbook,
      title={A Cookbook of Self-Supervised Learning}, 
      author={Randall Balestriero and Mark Ibrahim and Vlad Sobal and Ari Morcos and Shashank Shekhar and Tom Goldstein and Florian Bordes and Adrien Bardes and Gregoire Mialon and Yuandong Tian and Avi Schwarzschild and Andrew Gordon Wilson and Jonas Geiping and Quentin Garrido and Pierre Fernandez and Amir Bar and Hamed Pirsiavash and Yann LeCun and Micah Goldblum},
      year={2023},
      eprint={2304.12210},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{Rajamohan2023,
  title    = "Prediction of total knee replacement using deep learning analysis
              of knee {MRI}",
  author   = "Rajamohan, Haresh Rengaraj and Wang, Tianyu and Leung, Kevin and
              Chang, Gregory and Cho, Kyunghyun and Kijowski, Richard and
              Deniz, Cem M",
  abstract = "Current methods for assessing knee osteoarthritis (OA) do not
              provide comprehensive information to make robust and accurate
              outcome predictions. Deep learning (DL) risk assessment models
              were developed to predict the progression of knee OA to total
              knee replacement (TKR) over a 108-month follow-up period using
              baseline knee MRI. Participants of our retrospective study
              consisted of 353 case-control pairs of subjects from the
              Osteoarthritis Initiative with and without TKR over a 108-month
              follow-up period matched according to age, sex, ethnicity, and
              body mass index. A traditional risk assessment model was created
              to predict TKR using baseline clinical risk factors. DL models
              were created to predict TKR using baseline knee radiographs and
              MRI. All DL models had significantly higher (p < 0.001) AUCs than
              the traditional model. The MRI and radiograph ensemble model and
              MRI ensemble model (where TKR risk predicted by several
              contrast-specific DL models were averaged to get the ensemble TKR
              risk prediction) had the highest AUCs of 0.90 (80\% sensitivity
              and 85\% specificity) and 0.89 (79\% sensitivity and 86\%
              specificity), respectively, which were significantly higher (p <
              0.05) than the AUCs of the radiograph and multiple MRI models
              (where the DL models were trained to predict TKR risk using
              single contrast or 2 contrasts together as input). DL models
              using baseline MRI had a higher diagnostic performance for
              predicting TKR than a traditional model using baseline clinical
              risk factors and a DL model using baseline knee radiographs.",
  journal  = "Sci. Rep.",
  volume   =  13,
  number   =  1,
  pages    = "6922",
  month    =  apr,
  year     =  2023,
  language = "en"
}
@ARTICLE{Jamshidi2021,
  title     = "Machine learning-based individualized survival prediction model
               for total knee replacement in osteoarthritis: Data from the
               Osteoarthritis Initiative",
  author    = "Jamshidi, Afshin and Pelletier, Jean-Pierre and Labbe, Aurelie
               and Abram, Fran{\c c}ois and Martel-Pelletier, Johanne and
               Droit, Arnaud",
 
  journal   = "Arthritis Care Res. (Hoboken)",
  publisher = "Wiley",
  volume    =  73,
  number    =  10,
  pages     = "1518--1527",
  month     =  oct,
  year      =  2021,
  keywords  = "X-ray; feature selection; machine learning; magnetic resonance
               imaging; osteoarthritis; prediction; survival analysis; total
               knee replacement",
  copyright = "http://onlinelibrary.wiley.com/termsAndConditions\#vor",
  language  = "en"
}

@ARTICLE{Hu2023_deepKOA,
  title    = "{DeepKOA}: a deep-learning model for predicting progression in
              knee osteoarthritis using multimodal magnetic resonance images
              from the osteoarthritis initiative",
  author   = "Hu, Jiaping and Zheng, Chuanyang and Yu, Qingling and Zhong,
              Lijie and Yu, Keyan and Chen, Yanjun and Wang, Zhao and Zhang,
              Bin and Dou, Qi and Zhang, Xiaodong",
  journal  = "Quant. Imaging Med. Surg.",
  volume   =  13,
  number   =  8,
  pages    = "4852--4866",
  month    =  aug,
  year     =  2023,
  keywords = "Knee; deep learning; magnetic resonance imaging (MRI);
              osteoarthritis (OA); progression",
  language = "en"
}

@ARTICLE{Lester2008_OAI,
  title    = "Clinical research in {OA--the} {NIH} Osteoarthritis Initiative",
  author   = "Gayle Lester",
  journal  = "J. Musculoskelet. Neuronal Interact.",
  volume   =  8,
  number   =  4,
  pages    = "313--314",
  month    =  oct,
  year     =  2008,
  language = "en"
}
@online{MOSTdataset,
  title = {Multicenter Osteoarthritis Study (MOST) Public Data Sharing},
  url = {https://most.ucsf.edu/multicenter-osteoarthritis-study-most-public-data-sharing},
}

@inproceedings{Hedayati2023,
  title = "Estimating Time-to-Total Knee Replacement Surgery using Deep Learning",
  author = "Eisa Hedayati and Haresh Rajamohand and Lily Zhou and Kyunghyun Cho and Gregory Chang and Richard Kijowski and Cem M Deniz",
  booktitle = "Proceedings of the 2023 ISMRM \& ISMRT Annual Meeting \& Exhibition",
  year = 2023,
  address = "Toronto, Canada"
}
 
@ARTICLE{Cigdem2023Review,
  title     = "Artificial intelligence in knee osteoarthritis: A comprehensive
               review for 2022",
  author    = "Cigdem, Ozkan and Deniz, Cem M",
  journal   = "Osteoarthr. Imaging",
  publisher = "Elsevier BV",
  volume    =  3,
  number    =  3,
  pages     = "100161",
  month     =  sep,
  year      =  2023,
  copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  language  = "en"
}
@ARTICLE{Suresh2022,
  title     = "Survival prediction models: an introduction to discrete-time
               modeling",
  author    = "Suresh, Krithika and Severn, Cameron and Ghosh, Debashis",
  journal   = "BMC Med. Res. Methodol.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  22,
  number    =  1,
  pages     = "207",
  month     =  jul,
  year      =  2022,
  keywords  = "Cox proportional hazards; Machine learning; Random survival
               forest; Time-to-event",
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}
@ARTICLE{Heisinger2020,
  title     = "Predicting total knee replacement from symptomology and
               radiographic structural change using artificial neural
               networks-data from the osteoarthritis initiative ({OAI})",
  author    = "Heisinger, Stephan and Hitzl, Wolfgang and Hobusch, Gerhard M
               and Windhager, Reinhard and Cotofana, Sebastian",
  journal   = "J. Clin. Med.",
  publisher = "MDPI AG",
  volume    =  9,
  number    =  5,
  pages     = "1298",
  month     =  may,
  year      =  2020,
  keywords  = "Knee; Neural Networks; Osteoarthritis; Prediction model; Total
               Knee Replacement",
  language  = "en"
}
@ARTICLE{Mahmoud2023,
  title    = "Predicting total knee replacement at 2 and 5 years in
              osteoarthritis patients using machine learning",
  author   = "Mahmoud, Khadija and Alagha, M Abdulhadi and Nowinka, Zuzanna and
              Jones, Gareth",
  journal  = "BMJ Surg. Interv. Health Technol.",
  volume   =  5,
  number   =  1,
  pages    = "e000141",
  month    =  feb,
  year     =  2023,
  keywords = "Health Care Quality, Access, and Evaluation; Learning Curve;
              Orthopedic Devices; Patient Outcome Assessment; Technology
              Assessment, Biomedical",
  language = "en"
}
% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Qiang_Liu2022,
  title     = "Prediction models for the risk of total knee replacement:
               development and validation using data from multicentre cohort
               studies",
  author    = "Liu, Qiang and Chu, Hongling and LaValley, Michael P and Hunter,
               David J and Zhang, Hua and Tao, Liyuan and Zhan, Siyan and Lin,
               Jianhao and Zhang, Yuqing",
  journal   = "Lancet Rheumatol.",
  publisher = "Elsevier BV",
  volume    =  4,
  number    =  2,
  pages     = "e125--e134",
  month     =  feb,
  year      =  2022,
  language  = "en"
}
@ARTICLE{Chen_S2019,
  title     = "{Med3D}: Transfer learning for {3D} medical image analysis",
  author    = "Chen, Sihong and Ma, Kai and Zheng, Yefeng",
  publisher = "arXiv",
  year      =  2019
}
@ARTICLE{Kellgren1957,
  title     = "Radiological assessment of osteo-arthrosis",
  author    = "Jonas H. Kellgren and John Lawrence",
  journal   = "Ann. Rheum. Dis.",
  publisher = "BMJ",
  volume    =  16,
  number    =  4,
  pages     = "494--502",
  month     =  dec,
  year      =  1957,
  keywords  = "OSTEOARTHRITIS/diagnosis",
  language  = "en"
}
@ARTICLE{Altman1986,
  title     = "Development of criteria for the classification and reporting of
               osteoarthritis: Classification of osteoarthritis of the knee",
  author    = "Altman, R and Asch, E and Bloch, D and Bole, G and Borenstein, D
               and Brandt, K and Christy, W and Cooke, T D and Greenwald, R and
               Hochberg, M and Howell, D and Kaplan, D and Koopman, W and
               Longley, S and Mankin, H and McShane, D J and Medsger, T and
               Meenan, R and Mikkelsen, W and Moskowitz, R and Murphy, W and
               Rothschild, B and Segal, M and Sokoloff, L and Wolfe, F",
  journal   = "Arthritis Rheum.",
  publisher = "Wiley",
  volume    =  29,
  number    =  8,
  pages     = "1039--1049",
  month     =  aug,
  year      =  1986,
  language  = "en"
}
@ARTICLE{Felson1998,
  title     = "An update on the epidemiology of knee and hip osteoarthritis
               with a view to prevention",
  author    = "Felson, D T and Zhang, Y",
  journal   = "Arthritis Rheum.",
  publisher = "Wiley",
  volume    =  41,
  number    =  8,
  pages     = "1343--1355",
  month     =  aug,
  year      =  1998,
  language  = "en"
}
@ARTICLE{Tolpadi2020,
  title     = "Deep learning predicts total knee replacement from magnetic
               resonance images",
  author    = "Tolpadi, Aniket A and Lee, Jinhee J and Pedoia, Valentina and
               Majumdar, Sharmila",
  abstract  = "Knee Osteoarthritis (OA) is a common musculoskeletal disorder in
               the United States. When diagnosed at early stages, lifestyle
               interventions such as exercise and weight loss can slow OA
               progression, but at later stages, only an invasive option is
               available: total knee replacement (TKR). Though a generally
               successful procedure, only 2/3 of patients who undergo the
               procedure report their knees feeling ``normal'' post-operation,
               and complications can arise that require revision. This
               necessitates a model to identify a population at higher risk of
               TKR, particularly at less advanced stages of OA, such that
               appropriate treatments can be implemented that slow OA
               progression and delay TKR. Here, we present a deep learning
               pipeline that leverages MRI images and clinical and demographic
               information to predict TKR with AUC 0.834 $\pm$ 0.036 (p <
               0.05). Most notably, the pipeline predicts TKR with AUC 0.943
               $\pm$ 0.057 (p < 0.05) for patients without OA. Furthermore, we
               develop occlusion maps for case-control pairs in test data and
               compare regions used by the model in both, thereby identifying
               TKR imaging biomarkers. As such, this work takes strides towards
               a pipeline with clinical utility, and the biomarkers identified
               further our understanding of OA progression and eventual TKR
               onset.",
  journal   = "Sci. Rep.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  10,
  number    =  1,
  pages     = "6371",
  month     =  apr,
  year      =  2020,
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}
@ARTICLE{Hunter2011,
  title     = "Evolution of semi-quantitative whole joint assessment of knee
               {OA}: {MOAKS} ({MRI} Osteoarthritis Knee Score)",
  author    = "Hunter, D J and Guermazi, A and Lo, G H and Grainger, A J and
               Conaghan, P G and Boudreau, R M and Roemer, F W",
  journal   = "Osteoarthritis Cartilage",
  publisher = "Elsevier BV",
  volume    =  19,
  number    =  8,
  pages     = "990--1002",
  month     =  aug,
  year      =  2011,
  copyright = "https://www.elsevier.com/open-access/userlicense/1.0/",
  language  = "en"
}
@ARTICLE{Sneag2023,
  title    = "{MRI} advancements in musculoskeletal clinical and research
              practice",
  author   = "Sneag, Darryl B and Abel, Frederik and Potter, Hollis G and
              Fritz, Jan and Koff, Matthew F and Chung, Christine B and Pedoia,
              Valentina and Tan, Ek T",
  journal  = "Radiology",
  volume   =  308,
  number   =  2,
  pages    = "e230531",
  month    =  aug,
  year     =  2023,
  language = "en"
}
@ARTICLE{Wetstein2022,
  title     = "Deep learning-based breast cancer grading and survival analysis
               on whole-slide histopathology images",
  author    = "Wetstein, Suzanne C and de Jong, Vincent M T and Stathonikos,
               Nikolas and Opdam, Mark and Dackus, Gwen M H E and Pluim, Josien
               P W and van Diest, Paul J and Veta, Mitko",
  journal   = "Sci. Rep.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  12,
  number    =  1,
  pages     = "15102",
  month     =  sep,
  year      =  2022,
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}
@ARTICLE{Adeoye2022,
  title     = "Comparison of time-to-event machine learning models in
               predicting oral cavity cancer prognosis",
  author    = "Adeoye, John and Hui, Liuling and Koohi-Moghadam, Mohamad and
               Tan, Jia Yan and Choi, Siu-Wai and Thomson, Peter",
  journal   = "Int. J. Med. Inform.",
  publisher = "Elsevier BV",
  volume    =  157,
  number    =  104635,
  pages     = "104635",
  month     =  jan,
  year      =  2022,
  keywords  = "Artificial intelligence; Machine learning; Oral cavity cancer;
               Prognosis; Time-to-event",
  language  = "en"
}
@ARTICLE{Yeoh2023,
  title    = "Transfer learning-assisted {3D} deep learning models for knee
              osteoarthritis detection: Data from the osteoarthritis initiative",
  author   = "Yeoh, Pauline Shan Qing and Lai, Khin Wee and Goh, Siew Li and
              Hasikin, Khairunnisa and Wu, Xiang and Li, Pei",
  journal  = "Front. Bioeng. Biotechnol.",
  volume   =  11,
  pages    = "1164655",
  month    =  apr,
  year     =  2023,
  keywords = "convolutional neural network; deep learning; disease
              classification; knee osteoarthritis; magnetic resonance imaging",
  language = "en"
}
@ARTICLE{Yoon2023,
  title     = "Assessment of a novel deep learning-based software developed for
               automatic feature extraction and grading of radiographic knee
               osteoarthritis",
  author    = "Yoon, Ji Soo and Yon, Chang-Jin and Lee, Daewoo and Lee, Jae
               Joon and Kang, Chang Ho and Kang, Seung-Baik and Lee, Na-Kyoung
               and Chang, Chong Bum",
  journal   = "BMC Musculoskelet. Disord.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  24,
  number    =  1,
  pages     = "869",
  month     =  nov,
  year      =  2023,
  keywords  = "Artificial intelligence; Deep learning; Joint space narrowing;
               KL grade; Kellgren \& Lawrence classification; Knee
               osteoarthritis",
  copyright = "https://creativecommons.org/licenses/by/4.0",
  language  = "en"
}
@ARTICLE{Kijowski2023_DL,
  title    = "Deep learning applications in osteoarthritis imaging",
  author   = "Kijowski, Richard and Fritz, Jan and Deniz, Cem M",
  journal  = "Skeletal Radiol.",
  volume   =  52,
  number   =  11,
  pages    = "2225--2238",
  month    =  nov,
  year     =  2023,
  keywords = "Artificial intelligence; Deep learning; Knee; MRI;
              Osteoarthritis; X-rays",
  language = "en"
}
@ARTICLE{Chea2020,
  title     = "Current applications and future directions of deep learning in
               musculoskeletal radiology",
  author    = "Chea, Pauley and Mandell, Jacob C",
  journal   = "Skeletal Radiol.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  49,
  number    =  2,
  pages     = "183--197",
  month     =  feb,
  year      =  2020,
  keywords  = "Algorithms; Applications; Artificial intelligence; Convolutional
               neural networks; Deep learning; Musculoskeletal; Neural
               networks; Radiology",
  language  = "en"
}

@ARTICLE{62_Oak2013,
  title     = "Radiographic joint space width is correlated with 4-year
               clinical outcomes in patients with knee osteoarthritis: data
               from the osteoarthritis initiative",
  author    = "Oak, S R and Ghodadra, A and Winalski, C S and Miniaci, A and
               Jones, M H",
  abstract  = "OBJECTIVE: To evaluate if quantitative joint space width (JSW)
               measurements from radiographs correlate with 4-year Knee injury
               and Osteoarthritis Outcome Scores (KOOS) and clinical
               performance measures. METHOD: The study group consisted of 942
               patients with symptomatic knee osteoarthritis (OA). 4-year
               outcomes for six measures (KOOS Pain, Symptom, Quality of Life,
               and Function scores, 20-m walk pace, and chair stand time) were
               used to create six multiple linear regression models. Primary
               predictors were baseline minimum JSW and 4-year change in JSW
               measured from fixed flexion radiographs. Age, gender, body mass
               index (BMI), race, knee alignment, and baseline measures of the
               outcomes of interest were covariates. RESULTS: Lower baseline
               minimum JSW and a greater decrease in 4-year JSW significantly
               correlated with worse 4-year KOOS Pain, Symptom, and Quality of
               Life. With all other factors constant, a 4.1, 4.8, and 5.6 mm
               lower baseline JSW correlated with a clinically significant
               eight-point drop in 4-year KOOS Pain, Symptom, and Quality of
               Life scores respectively. Additionally, a 3.5, 3.1, and 4.0 mm
               loss of JSW over 4 years correlated with a clinically
               significant eight-point drop in 4-year KOOS Pain, Symptom, and
               Quality of Life scores respectively. CONCLUSIONS: Our results
               indicate quantitative radiographic JSW measurements correlate
               with 4-year clinical outcomes. Since patients with narrower JSW
               at the onset of study had lower KOOS scores at 4 years even
               after controlling for 4-year change in JSW and baseline KOOS
               scores, clinical outcomes in knee OA may be predetermined once
               the disease process begins. These findings suggest early
               treatment with disease modifying therapies may be necessary to
               influence outcomes.",
  journal   = "Osteoarthritis Cartilage",
  publisher = "Elsevier BV",
  volume    =  21,
  number    =  9,
  pages     = "1185--1190",
  month     =  sep,
  year      =  2013,
  keywords  = "Clinical progression; Joint space width; Knee osteoarthritis;
               X-ray",
  copyright = "https://www.elsevier.com/open-access/userlicense/1.0/",
  language  = "en"
}

@ARTICLE{65_Buck2010,
  title     = "An efficient subset of morphological measures for articular
               cartilage in the healthy and diseased human knee",
  author    = "Buck, Robert J and Wyman, Bradley T and Le Graverand,
               Marie-Pierre Hellio and Wirth, Wolfgang and Eckstein, Felix and
               {A9001140 Investigators}",
  abstract  = "The relationship between three-dimensional, MRI-based
               morphologic measurements commonly taken of knee cartilage was
               examined to determine whether a subset of variables fully
               reflects differences observed in cartilage in cross-sectional
               and longitudinal studies. The benefits of a subset of measures
               include increased statistical power due to reduced multiple
               comparisons, improved understanding of relationships between the
               morphologic measures of articular knee cartilage, and greater
               efficiency in reporting results. One hundred fifty-two women (77
               healthy and 75 with knee osteoarthritis) had coronal 3-T MR
               images of the knee acquired at baseline and at 24 months.
               Measures of femorotibial cartilage morphology (surface area,
               thickness, volume, etc.) were determined in the medial and
               lateral tibia and femur. Cartilage thickness (mean cartilage
               thickness over the total area of the [subchondral] bone), total
               subchondral bone area, and percentage of denuded area of the
               subchondral bone were found to explain over 90\% of the
               cross-sectional and longitudinal variation observed in other
               measures of cartilage morphology commonly reported in knee
               osteoarthritis. Hence, these three measures of cartilage
               morphology explain nearly all variation in a larger set of
               common cartilage morphology measures both cross-sectionally and
               longitudinally, both in healthy and in osteoarthritic knees.
               These variables hence define an efficient subset for describing
               structural status and change in osteoarthritic cartilage.",
  journal   = "Magn. Reson. Med.",
  publisher = "Wiley",
  volume    =  63,
  number    =  3,
  pages     = "680--690",
  month     =  mar,
  year      =  2010,
  copyright = "http://onlinelibrary.wiley.com/termsAndConditions\#vor",
  language  = "en"
}
@ARTICLE{66_Eckstein2009,
  title     = "One year change of knee cartilage morphology in the first
               release of participants from the Osteoarthritis Initiative
               progression subcohort: association with sex, body mass index,
               symptoms and radiographic osteoarthritis status",
  author    = "Eckstein, F and Maschek, S and Wirth, W and Hudelmaier, M and
               Hitzl, W and Wyman, B and Nevitt, M and Le Graverand, M-P Hellio
               and {the OAI Investigator Group}",
  abstract  = "Objective:The Osteoarthritis Initiative (OAI) is a multicentre
               study targeted at identifying biomarkers for evaluating the
               progression and risk factors of symptomatic knee OA. Here
               cartilage loss using 3 Tesla (3 T) MRI is analysed over 1 year
               in a subset of the OAI, together with its association with
               various risk factors.Methods:An age- and gender-stratified
               subsample of the OAI progression subcohort (79 women and 77 men,
               mean (SD) age 60.9 (9.9) years, body mass index (BMI) 30.3
               (4.7)) with both frequent symptoms and radiographic OA in at
               least one knee was studied. Coronal FLASHwe (fast low angle shot
               with water excitation) MRIs of the right knee were acquired at 3
               T. Seven readers segmented tibial and femoral cartilages blinded
               to order of acquisition. Segmentations were quality controlled
               by one expert.Results:The reduction in mean cartilage thickness
               (ThC) was greater (p = 0.004) in the medial than in the lateral
               compartment, greater (p = 0.001) in the medial femur (−1.9\%)
               than in the medial tibia (−0.5\%) and greater (p = 0.011) in the
               lateral tibia (−0.7\%) than in the lateral femur (0.1\%).
               Multifactorial analysis of variance did not reveal significant
               differences in the rate of change in ThC by sex, BMI, symptoms
               and radiographic knee OA status. Knees with Kellgren--Lawrence
               grade 2 or 3 and with a BMI >30 tended to display greater
               changes.Conclusions:In this sample of the OAI progression
               subcohort, the greatest, but overall very modest, rate of
               cartilage loss was observed in the weight-bearing medial femoral
               condyle. Knees with radiographic OA in obese participants showed
               trends towards higher rates of change than those of other
               participants, but these trends did not reach statistical
               significance.",
  journal   = "Ann. Rheum. Dis.",
  publisher = "BMJ",
  volume    =  68,
  number    =  5,
  pages     = "674--679",
  month     =  may,
  year      =  2009,
  language  = "en"
}
@ARTICLE{67_Eckstein2015,
  title     = "Brief report: Cartilage thickness change as an imaging biomarker
               of knee osteoarthritis progression: Data from the foundation for
               the national institutes of health osteoarthritis biomarkers
               consortium",
  author    = "Eckstein, F and Collins, J E and Nevitt, M C and Lynch, J A and
               Kraus, V B and Katz, J N and Losina, E and Wirth, W and
               Guermazi, A and Roemer, F W and Hunter, D J and {for the FNIH OA
               Biomarkers Consortium}",
  abstract  = "ObjectiveTo investigate the association of cartilage thickness
               change over 24 months, as determined by magnetic resonance
               imaging (MRI), with knee osteoarthritis (OA) progression at
               24--48 months.MethodsThis nested case--control study included
               600 knees with a baseline Kellgren/Lawrence (K/L) grade of 1--3
               from 600 Osteoarthritis Initiative (OAI) participants. Case
               knees (n = 194) had both medial tibiofemoral radiographic joint
               space loss ($\geq$0.7 mm) and a persistent increase in the
               Western Ontario and McMaster Universities Osteoarthritis Index
               pain score ($\geq$9 on a 0--100 scale) 24--48 months from
               baseline. Control knees (n = 406) included 200 with neither
               radiographic nor pain progression, 103 with radiographic
               progression only, and 103 with pain progression only. Medial and
               lateral femorotibial cartilage was segmented from sagittal 3T
               MRIs at baseline, 12 months, and 24 months. Logistic regression
               was used to assess the association of change in cartilage
               thickness, with a focus on the central medial femorotibial
               compartment, and OA progression.ResultsCentral medial
               femorotibial compartment thickness loss was significantly
               associated with case status, with an odds ratio (OR) of 1.9
               (95\% confidence interval [95\% CI] 1.6--2.3) (P P P P
               ConclusionOur findings indicate that loss of medial femorotibial
               cartilage thickness over 24 months is associated with the
               combination of radiographic and pain progression in the knee,
               with a stronger association for radiographic progression.",
  journal   = "Arthritis Rheumatol.",
  publisher = "Wiley",
  volume    =  67,
  number    =  12,
  pages     = "3184--3189",
  month     =  dec,
  year      =  2015,
  copyright = "http://onlinelibrary.wiley.com/termsAndConditions\#vor",
  language  = "en"
}

@ARTICLE{91_Dore2010,
  title     = "Bone marrow lesions predict site-specific cartilage defect
               development and volume loss: a prospective study in older adults",
  author    = "Dore, Dawn and Martens, Ashleigh and Quinn, Stephen and Ding,
               Changhai and Winzenberg, Tania and Zhai, Guangju and Pelletier,
               Jean-Pierre and Martel-Pelletier, Johanne and Abram, Fran{\c
               c}ois and Cicuttini, Flavia and Jones, Graeme",
  abstract  = "INTRODUCTION: Recent evidence suggests that bone marrow lesions
               (BMLs) play a pivotal role in knee osteoarthritis (OA). The aims
               of this study were to determine: 1) whether baseline BML
               presence and/or severity predict site-specific cartilage defect
               progression and cartilage volume loss; and 2) whether baseline
               cartilage defects predict site-specific BML progression.
               METHODS: A total of 405 subjects (mean age 63 years, range 52 to
               79) were measured at baseline and approximately 2.7 years later.
               Magnetic resonance imaging (MRI) of the right knee was performed
               to measure knee cartilage volume, cartilage defects (0 to 4),
               and BMLs (0 to 3) at the medial tibial (MT), medial femoral
               (MF), lateral tibial (LT), and lateral femoral (LF) sites.
               Logistic regression and generalized estimating equations were
               used to examine the relationship between BMLs and cartilage
               defects and cartilage volume loss. RESULTS: At all four sites,
               baseline BML presence predicted defect progression (odds ratio
               (OR) 2.4 to 6.4, all P < 0.05), and cartilage volume loss (-0.9
               to -2.9\% difference per annum, all P < 0.05) at the same site.
               In multivariable analysis, there was a significant relationship
               between BML severity and defect progression at all four sites
               (OR 1.8 to 3.2, all P < 0.05) and BML severity and cartilage
               volume loss at the MF, LT, and LF sites ($\beta$ -22.1 to -42.0,
               all P < 0.05). Additionally, baseline defect severity predicted
               BML progression at the MT and LF sites (OR 3.3 to 3.7, all P <
               0.01). Lastly, there was a greater increase in cartilage volume
               loss at the MT and LT sites when both larger defects and BMLs
               were present at baseline (all P < 0.05). CONCLUSIONS: Baseline
               BMLs predicted site-specific defect progression and cartilage
               volume loss in a dose-response manner suggesting BMLs may have a
               local effect on cartilage homeostasis. Baseline defects
               predicted site-specific BML progression, which may represent
               increased bone loading adjacent to defects. These results
               suggest BMLs and defects are interconnected and play key roles
               in knee cartilage volume loss; thus, both should be considered
               targets for intervention.",
  journal   = "Arthritis Res. Ther.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  12,
  number    =  6,
  pages     = "R222",
  month     =  dec,
  year      =  2010,
  language  = "en"
}

@ARTICLE{92_Tanamas2010,
  title     = "Bone marrow lesions in people with knee osteoarthritis predict
               progression of disease and joint replacement: a longitudinal
               study",
  author    = "Tanamas, Stephanie K and Wluka, Anita E and Pelletier,
               Jean-Pierre and Pelletier, Johanne M and Abram, Fran{\c c}ois
               and Berry, Patricia A and Wang, Yuanyuan and Jones, Graeme and
               Cicuttini, Flavia M",
  abstract  = "OBJECTIVES: The presence of bone marrow lesions (BMLs) has been
               linked to pain and progression of knee OA. The aim of this study
               was to determine the relationship between BMLs and longitudinal
               change in tibial cartilage volume and risk of knee joint
               replacement in subjects with knee OA. METHODS: One hundred and
               nine men and women with symptomatic knee OA were recruited. The
               same knee was imaged using MRI at baseline and ∼2 years later.
               Tibial cartilage volume and BMLs were measured. Knee joint
               replacement over 4 years was determined. RESULTS: The mean age
               of the subjects at baseline was 63.2 (s.d. 10.3) years. BMLs
               were present in 66\% of the subjects. Cross-sectionally, BMLs
               were negatively associated with both medial (regression
               coefficient -121.4; 95\% CI -183.8, -859.1; P<0.001) and lateral
               (regression coefficient -142.1; 95\% CI -241.8, -42.4; P=0.01)
               tibial cartilage volume data. Longitudinally, for every 1-score
               increase in baseline BML severity (range 0-4), the annual total
               tibial cartilage loss was increased by 1.14\% (95\% CI 0.29\%,
               1.87\%; P=0.01). The risk of knee joint replacement over 4 years
               increased with increasing BML score (odds ratio 1.57; 95\% CI
               1.04, 2.35; P=0.03). CONCLUSION: The prevalence and severity of
               BMLs are associated with less tibial cartilage volume and
               greater cartilage loss over 2 years. Moreover, severity of BMLs
               was positively associated with risk of knee joint replacement
               over 4 years. This provides further support for the importance
               of BMLs in identifying those with OA most likely to progress.
               Identifying factors that prevent or reduce the severity of BMLs
               may provide an important target in the prevention of disease
               progression and treatment of OA, and the subsequent need for
               arthroplasty.",
  journal   = "Rheumatology (Oxford)",
  publisher = "Oxford University Press (OUP)",
  volume    =  49,
  number    =  12,
  pages     = "2413--2419",
  month     =  dec,
  year      =  2010,
  language  = "en"
}
@ARTICLE{93_Wluka2009,
  title     = "Bone marrow lesions predict increase in knee cartilage defects
               and loss of cartilage volume in middle-aged women without knee
               pain over 2 years",
  author    = "Wluka, A E and Hanna, F and Davies-Tuck, M and Wang, Y and Bell,
               R J and Davis, S R and Adams, J and Cicuttini, F M",
  abstract  = "OBJECTIVE: Bone marrow lesions (BML) are important in
               established knee osteoarthritis, predicting pain and progression
               of disease. Whether BML are also associated with longitudinal
               changes in knee structure in an asymptomatic population is
               unknown. METHODS: 148 healthy pain-free women in middle age with
               no history of knee injury or clinical knee osteoarthritis who
               had a magnetic resonance imaging (MRI) scan performed on their
               dominant knee at baseline, had another MRI 2 years later to
               assess whether having a BML present at baseline affected change
               in tibiofemoral cartilage defects and tibial cartilage volume.
               RESULTS: BML were present in 14.9\% of women at baseline. The
               risk of progression of total tibiofemoral cartilage defects was
               significantly higher when a very large BML was present (odds
               ratio 5.55, 95\% CI 1.04 to 29.6) compared with when no BML was
               present, after adjusting for potential confounders. In the
               lateral compartment, the rate of cartilage volume loss was
               significantly greater when a BML was present after adjusting for
               confounders (regression coefficient 39.2 mm(3), 95\% CI 11.1 to
               67.2, p = 0.007). CONCLUSIONS: In healthy women without pain at
               baseline, large BML were associated with both progression of
               cartilage defects in the whole tibiofemoral joint and more rapid
               lateral tibial cartilage loss. These data suggest that the
               relationship between BML and knee cartilage in healthy women is
               similar to that described in established osteoarthritis. It is
               possible that BML may predict an increased risk of knee
               osteoarthritis and facilitate the identification of novel
               interventions to prevent disease.",
  journal   = "Ann. Rheum. Dis.",
  publisher = "BMJ",
  volume    =  68,
  number    =  6,
  pages     = "850--855",
  month     =  jun,
  year      =  2009,
  language  = "en"
}

@ARTICLE{94_Wluka2008,
  title     = "Bone marrow lesions predict progression of cartilage defects and
               loss of cartilage volume in healthy middle-aged adults without
               knee pain over 2 yrs",
  author    = "Wluka, A E and Wang, Y and Davies-Tuck, M and English, D R and
               Giles, G G and Cicuttini, F M",
  abstract  = "OBJECTIVE: In knee OA, the presence of bone marrow lesions
               (BMLs) predicts pain and progression of disease. Their
               occurrence has been described in healthy, pain-free subjects,
               but whether their presence affects change in cartilage is
               unknown. METHODS: Two hundred and seventy-one healthy
               community-dwelling adults with no history of knee injury, knee
               pain or clinical knee OA had an MRI performed on their dominant
               knee at baseline and 2 yrs later to assess the relationship
               between the presence of BMLs at baseline and change in
               tibiofemoral cartilage defects and tibial cartilage volume over
               2 yrs. RESULTS: BMLs were present in 37 (14\%) subjects.
               Cartilage defects were more likely to progress rather than
               remain stable or regress in subjects with BMLs compared with
               those without BMLs (P = 0.04). The odds of cartilage defects
               progressing in the tibiofemoral compartment of the knee where
               BMLs were present compared with where BMLs were absent was 2.6
               (95\% CI 1.2, 5.3; P = 0.01). Where 'very large' BMLs were
               present, there was a trend for increased annual tibial cartilage
               volume loss (46.4 mm(3)/yr; P = 0.07). CONCLUSIONS: These data
               suggest that BMLs are associated with change in knee cartilage
               over 2 yrs in asymptomatic subjects. Increased progression of
               cartilage defects is seen with increasing size of BMLs. It will
               be important to determine in future studies whether BMLs
               directly cause change in cartilage over 2 yrs, or act as a
               marker of another factor that facilitates these changes.",
  journal   = "Rheumatology (Oxford)",
  publisher = "Oxford University Press (OUP)",
  volume    =  47,
  number    =  9,
  pages     = "1392--1396",
  month     =  sep,
  year      =  2008,
  language  = "en"
}
@ARTICLE{95_Baranyay2007,
  title     = "Association of bone marrow lesions with knee structures and risk
               factors for bone marrow lesions in the knees of clinically
               healthy, community-based adults",
  author    = "Baranyay, Ferencz J and Wang, Yuanyuan and Wluka, Anita E and
               English, Dallas R and Giles, Graham G and Sullivan, Richard O
               and Cicuttini, Flavia M",
  abstract  = "OBJECTIVES: Subchondral bone marrow lesions (BML) are involved
               in pain and progression of knee osteoarthritis (OA). Little is
               known about their role in the knee in those without clinical OA.
               Our aim was to examine the prevalence and risk factors for BML,
               and their relationship with other knee structures in
               community-based adults without clinical OA. METHODS: Two hundred
               ninety-seven healthy subjects without knee pain or injury were
               recruited from an existing community-based cohort recruited at
               baseline in 1990-1994. Subjects with a single magnetic resonance
               imaging (MRI) of their dominant knee at follow-up were studied
               in 2003-2004. BML, cartilage defects, cartilage volume, and bone
               area of the knee were assessed using MRI. RESULTS: Thirty-nine
               subjects (13\%) had evidence of BML. BML were associated with
               the presence of cartilage defects in the medial (odds ratio (OR)
               1.80, P = 0.004) and lateral (OR 1.45, P = 0.04) tibiofemoral
               compartments, but not cartilage volume. BML were positively
               associated with total tibial bone area (OR 1.22, P = 0.02).
               Increasing age (OR 1.10, P < 0.001), male gender (OR 3.86, P =
               0.01), and increasing body height (OR 1.07, P = 0.03) were
               independently associated with BML in the total tibiofemoral
               compartment. CONCLUSIONS: BML are present in the knees of
               community-based adults without clinical OA and are strongly
               associated with tibiofemoral cartilage defects. Risk factors for
               BML were age, male gender, and body height. Longitudinal studies
               will be needed to clarify the role of BML in structural change
               of the knee and how this relates to the pathogenesis of
               symptomatic knee OA.",
  journal   = "Semin. Arthritis Rheum.",
  publisher = "Elsevier BV",
  volume    =  37,
  number    =  2,
  pages     = "112--118",
  month     =  oct,
  year      =  2007,
  language  = "en"
}

@ARTICLE{Chaudhari2021-qp,
  title     = "Diagnostic accuracy of quantitative multicontrast 5-minute knee
               {MRI} using prospective artificial intelligence image quality
               enhancement",
  author    = "Chaudhari, Akshay S and Grissom, Murray J and Fang, Zhongnan and
               Sveinsson, Bragi and Lee, Jin Hyung and Gold, Garry E and
               Hargreaves, Brian A and Stevens, Kathryn J",
  abstract  = "BACKGROUND. Potential approaches for abbreviated knee MRI,
               including prospective acceleration with deep learning, have
               achieved limited clinical implementation. OBJECTIVE. The
               objective of this study was to evaluate the interreader
               agreement between conventional knee MRI and a 5-minute 3D
               quantitative double-echo steady-state (qDESS) sequence with
               automatic T2 mapping and deep learning
               super-resolutionaugmentation and to compare the diagnostic
               performance of the two methods regarding findings from
               arthroscopic surgery. METHODS. Fifty-one patients with knee pain
               underwent knee MRI that included an additional 3D qDESS sequence
               with automatic T2 mapping. Fourier interpolation was followed by
               prospective deep learning super resolution to enhance qDESS
               slice resolution twofold. A musculoskeletal radiologist and a
               radiology resident performed independent retrospective
               evaluations of articular cartilage, menisci, ligaments, bones,
               extensor mechanism, and synovium using conventional MRI.
               Following a 2-month washout period, readers reviewed qDESS
               images alone followed by qDESS with the automatic T2 maps.
               Interreader agreement between conventional MRI and qDESS was
               computed using percentage agreement and Cohen kappa. The
               sensitivity and specificity of conventional MRI, qDESS alone,
               and qDESS plus T2 mapping were compared with arthroscopic
               findings using exact McNemar tests. RESULTS. Conventional MRI
               and qDESS showed 92\% agreement in evaluating all tissues. Kappa
               was 0.79 (95\% CI, 0.76-0.81) across all imaging findings. In 43
               patients who underwent arthroscopy, sensitivity and specificity
               were not significantly different (p = .23 to > .99) between
               conventional MRI (sensitivity, 58-93\%; specificity, 27-87\%)
               and qDESS alone (sensitivity, 54-90\%; specificity, 23-91\%) for
               cartilage, menisci, ligaments, and synovium. For grade 1
               cartilage lesions, sensitivity and specificity were 33\% and
               56\%, respectively, for conventional MRI; 23\% and 53\% for
               qDESS (p = .81); and 46\% and 39\% for qDESS with T2 mapping (p
               = .80). For grade 2A lesions, values were 27\% and 53\% for
               conventional MRI, 26\% and 52\% for qDESS (p = .02), and 58\%
               and 40\% for qDESS with T2 mapping (p < .001). CONCLUSION. The
               qDESS method prospectively augmented with deep learning showed
               strong interreader agreement with conventional knee MRI and
               near-equivalent diagnostic performance regarding arthroscopy.
               The ability of qDESS to automatically generate T2 maps increases
               sensitivity for cartilage abnormalities. CLINICAL IMPACT. Using
               prospective artificial intelligence to enhance qDESS image
               quality may facilitate an abbreviated knee MRI protocol while
               generating quantitative T2 maps.",
  journal   = "AJR Am. J. Roentgenol.",
  publisher = "American Roentgen Ray Society",
  volume    =  216,
  number    =  6,
  pages     = "1614--1625",
  month     =  jun,
  year      =  2021,
  keywords  = "MRI; artificial intelligence; biomarkers; knee injuries;
               radiology",
  language  = "en"
}

@MISC{NYULH_Dataset,
  howpublished = "\url{https://nda.nih.gov/oai}"
}

@ARTICLE{Park2021_KaplanMeier,
  title     = "Mistakes to avoid for accurate and transparent reporting of
               survival analysis in imaging research",
  author    = "Park, Seong Ho and Han, Kyunghwa and Park, Seo Young",
  journal   = "Korean J. Radiol.",
  publisher = "The Korean Society of Radiology",
  volume    =  22,
  number    =  10,
  pages     = "1587--1593",
  month     =  oct,
  year      =  2021,
  keywords  = "Error; Guide; Mistake; Reporting; Survival analysis; Tip",
  language  = "en"
}

@INPROCEEDINGS{He2015_Resnet,
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Deep Residual Learning for Image Recognition}, 
  year={2016},
  volume={},
  number={},
  pages={770-778},
  keywords={Training;Degradation;Complexity theory;Image recognition;Neural networks;Visualization;Image segmentation},
  doi={10.1109/CVPR.2016.90}}

@article{Tran2017_3DResnet,
  author    = {Du Tran and
               Heng Wang and
               Lorenzo Torresani and
               Jamie Ray and
               Yann LeCun and
               Manohar Paluri},
  title     = {A Closer Look at Spatiotemporal Convolutions for Action Recognition},
  journal   = {CoRR},
  volume    = {abs/1711.11248},
  year      = {2017},
  url       = {http://arxiv.org/abs/1711.11248},
  archivePrefix = {arXiv},
  eprint    = {1711.11248},
  timestamp = {Mon, 13 Aug 2018 16:46:39 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1711-11248.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@ARTICLE{Vaswani2017-hj,
  title        = "Attention is all you need",
  author       = "Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and
                  Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and
                  Kaiser, Lukasz and Polosukhin, Illia",
  abstract     = "The dominant sequence transduction models are based on
                  complex recurrent or convolutional neural networks in an
                  encoder-decoder configuration. The best performing models
                  also connect the encoder and decoder through an attention
                  mechanism. We propose a new simple network architecture, the
                  Transformer, based solely on attention mechanisms, dispensing
                  with recurrence and convolutions entirely. Experiments on two
                  machine translation tasks show these models to be superior in
                  quality while being more parallelizable and requiring
                  significantly less time to train. Our model achieves 28.4
                  BLEU on the WMT 2014 English-to-German translation task,
                  improving over the existing best results, including ensembles
                  by over 2 BLEU. On the WMT 2014 English-to-French translation
                  task, our model establishes a new single-model
                  state-of-the-art BLEU score of 41.8 after training for 3.5
                  days on eight GPUs, a small fraction of the training costs of
                  the best models from the literature. We show that the
                  Transformer generalizes well to other tasks by applying it
                  successfully to English constituency parsing both with large
                  and limited training data.",
  year         =  2017,
  primaryClass = "cs.CL",
  eprint       = "1706.03762"
}

@ARTICLE{Du2024-df,
  title        = "{TIP}: Tabular-image pre-training for multimodal
                  classification with incomplete data",
  author       = "Du, Siyi and Zheng, Shaoming and Wang, Yinsong and Bai,
                  Wenjia and O'Regan, Declan P and Qin, Chen",
  abstract     = "Images and structured tables are essential parts of
                  real-world databases. Though tabular-image representation
                  learning is promising to create new insights, it remains a
                  challenging task, as tabular data is typically heterogeneous
                  and incomplete, presenting significant modality disparities
                  with images. Earlier works have mainly focused on simple
                  modality fusion strategies in complete data scenarios,
                  without considering the missing data issue, and thus are
                  limited in practice. In this paper, we propose TIP, a novel
                  tabular-image pre-training framework for learning multimodal
                  representations robust to incomplete tabular data.
                  Specifically, TIP investigates a novel self-supervised
                  learning (SSL) strategy, including a masked tabular
                  reconstruction task for tackling data missingness, and
                  image-tabular matching and contrastive learning objectives to
                  capture multimodal information. Moreover, TIP proposes a
                  versatile tabular encoder tailored for incomplete,
                  heterogeneous tabular data and a multimodal interaction
                  module for inter-modality representation learning.
                  Experiments are performed on downstream multimodal
                  classification tasks using both natural and medical image
                  datasets. The results show that TIP outperforms
                  state-of-the-art supervised/SSL image/multimodal algorithms
                  in both complete and incomplete data scenarios. Our code is
                  available at https://github.com/siyi-wind/TIP.",
  year         =  2024,
  primaryClass = "cs.CV",
  eprint       = "2407.07582"
}

@ARTICLE{Arik2019-vh,
  title        = "{TabNet}: Attentive Interpretable Tabular Learning",
  author       = "Arik, Sercan O and Pfister, Tomas",
  abstract     = "We propose a novel high-performance and interpretable
                  canonical deep tabular data learning architecture, TabNet.
                  TabNet uses sequential attention to choose which features to
                  reason from at each decision step, enabling interpretability
                  and more efficient learning as the learning capacity is used
                  for the most salient features. We demonstrate that TabNet
                  outperforms other neural network and decision tree variants
                  on a wide range of non-performance-saturated tabular datasets
                  and yields interpretable feature attributions plus insights
                  into the global model behavior. Finally, for the first time
                  to our knowledge, we demonstrate self-supervised learning for
                  tabular data, significantly improving performance with
                  unsupervised representation learning when unlabeled data is
                  abundant.",
  year         =  2019,
  primaryClass = "cs.LG",
  eprint       = "1908.07442"
}
@ARTICLE{Wolf2022-pc,
  title     = "{DAFT}: A universal module to interweave tabular data and {3D}
               images in {CNNs}",
  author    = "Wolf, Tom Nuno and P{\"o}lsterl, Sebastian and Wachinger,
               Christian ",
  abstract  = "Prior work on Alzheimer's Disease (AD) has demonstrated that
               convolutional neural networks (CNNs) can leverage the
               high-dimensional image information for diagnosing patients.
               Beside such data-driven approaches, many established biomarkers
               exist and are typically represented as tabular data, such as
               demographics, genetic alterations, or laboratory measurements
               from cerebrospinal fluid. However, little research has focused
               on the effective integration of tabular data into existing CNN
               architectures to improve patient diagnosis. We introduce the
               Dynamic Affine Feature Map Transform (DAFT), a general-purpose
               module for CNNs that incites or represses high-level concepts
               learned from a 3D image by conditioning feature maps of a
               convolutional layer on both a patient's image and tabular
               clinical information. This is achieved by using an auxiliary
               neural network that outputs a scaling factor and offset to
               dynamically apply an affine transformation to the feature maps
               of a convolutional layer. In our experiments on AD diagnosis and
               time-to-dementia prediction, we show that the DAFT is highly
               effective in combining 3D image and tabular information by
               achieving a mean balanced accuracy of 0.622 for diagnosis, and
               mean c-index of 0.748 for time-to-dementia prediction, thus
               outperforming all baseline methods. Finally, our extensive
               ablation study and empirical experiments reveal that the
               performance improvement due to the DAFT is robust with respect
               to many design choices.",
  journal   = "Neuroimage",
  publisher = "Elsevier BV",
  volume    =  260,
  number    =  119505,
  pages     = "119505",
  month     =  oct,
  year      =  2022,
  keywords  = "Alzheimer's disease; Convolutional neural networks; Deep
               learning; Disease prediction; Magnetic resonance imaging;
               Tabular data; Time-to-event analysis",
  copyright = "http://creativecommons.org/licenses/by/4.0/",
  language  = "en"
}
@ARTICLE{Kita2023-qt,
  title     = "Bimodal artificial intelligence using {TabNet} for
               differentiating spinal cord tumors-Integration of patient
               background information and images",
  author    = "Kosuke Kita and Takahito Fujimori and Yuki Suzuki and Yuya Kanie and Shota Takenaka and Takashi Kaito and Takuyu Taki and Yuichiro Ukon and Masayuki Furuya and Hirokazu Saiwai and Nozomu Nakajima and Tsuyoshi Sugiura and Hiroyuki Ishiguro and Takashi Kamatani and Hiroyuki Tsukazaki and Yusuke Sakai and Haruna Takami and Daisuke Tateiwa and Kunihiko Hashimoto and Tomohiro Wataya and Daiki Nishigaki and Junya Sato and Masaki Hoshiyama and Noriyuki Tomiyama and Seiji Okada and Shoji Kido",
  abstract  = "We proposed a bimodal artificial intelligence that integrates
               patient information with images to diagnose spinal cord tumors.
               Our model combines TabNet, a state-of-the-art deep learning
               model for tabular data for patient information, and a
               convolutional neural network for images. As training data, we
               collected 259 spinal tumor patients (158 for schwannoma and 101
               for meningioma). We compared the performance of the image-only
               unimodal model, table-only unimodal model, bimodal model using a
               gradient-boosting decision tree, and bimodal model using TabNet.
               Our proposed bimodal model using TabNet performed best (area
               under the receiver-operating characteristic curve [AUROC]: 0.91)
               in the training data and significantly outperformed the
               physicians' performance. In the external validation using 62
               cases from the other two facilities, our bimodal model showed an
               AUROC of 0.92, proving the robustness of the model. The bimodal
               analysis using TabNet was effective for differentiating spinal
               tumors.",
  journal   = "iScience",
  publisher = "Elsevier BV",
  volume    =  26,
  number    =  10,
  pages     = "107900",
  month     =  oct,
  year      =  2023,
  keywords  = "Artificial intelligence; Cancer; Image-guided intervention",
  copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  language  = "en"
}
@INPROCEEDINGS{Felfeliyan2024,
  author={Felfeliyan, Banafshe and Zhou, Yuyue and Ghosh, Shrimanti and Kupper, Jessica and Liu, Shaobo and Hareendranathan, Abhilash R. and Jaremko, Jacob L.},
  booktitle={2024 IEEE International Symposium on Biomedical Imaging (ISBI)}, 
  title={Application of Vision-Language Models for Assessing Osteoarthritis}, 
  year={2024},
  volume={},
  number={},
  pages={1-4},
  keywords={Biological system modeling;Medical services;Predictive models;History;Osteoarthritis;Diagnostic radiography;X-ray imaging;Vision-Language;Multi-modal Representation Learning;Osteoarthritis;Radiology Report},
  doi={10.1109/ISBI56570.2024.10635386}}
@ARTICLE{Panfilov2023-gl,
  title        = "End-to-end prediction of knee Osteoarthritis progression with
                  multi-modal transformers",
  author       = "Panfilov, Egor and Saarakkala, Simo and Nieminen, Miika T and
                  Tiulpin, Aleksei",
  abstract     = "Knee Osteoarthritis (KOA) is a highly prevalent chronic
                  musculoskeletal condition with no currently available
                  treatment. The manifestation of KOA is heterogeneous and
                  prediction of its progression is challenging. Current
                  literature suggests that the use of multi-modal data and
                  advanced modeling methods, such as the ones based on Deep
                  Learning, has promise in tackling this challenge. To date,
                  however, the evidence on the efficacy of this approach is
                  limited. In this study, we leveraged recent advances in Deep
                  Learning and, using a Transformer approach, developed a
                  unified framework for the multi-modal fusion of knee imaging
                  data. Subsequently, we analyzed its performance across a
                  range of scenarios by investigating multiple progression
                  horizons -- from short-term to long-term. We report our
                  findings using a large cohort (n=2421-3967) derived from the
                  Osteoarthritis Initiative dataset. We show that structural
                  knee MRI allows identifying radiographic KOA progressors on
                  par with multi-modal fusion approaches, achieving an area
                  under the ROC curve (ROC AUC) of 0.70-0.76 and Average
                  Precision (AP) of 0.15-0.54 in 2-8 year horizons. Progression
                  within 1 year was better predicted with a multi-modal method
                  using X-ray, structural, and compositional MR images -- ROC
                  AUC of 0.76(0.04), AP of 0.13(0.04) -- or via clinical data.
                  Our follow-up analysis generally shows that prediction from
                  the imaging data is more accurate for post-traumatic
                  subjects, and we further investigate which subject subgroups
                  may benefit the most. The present study provides novel
                  insights into multi-modal imaging of KOA and brings a unified
                  data-driven framework for studying its progression in an
                  end-to-end manner, providing new tools for the design of more
                  efficient clinical trials. The source code of our framework
                  and the pre-trained models are made publicly available.",
  year         =  2023,
  primaryClass = "eess.IV",
  eprint       = "2307.00873"
}
@ARTICLE{Akiba2019-co,
  title        = "Optuna: A next-generation hyperparameter optimization
                  framework",
  author       = "Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and
                  Ohta, Takeru and Koyama, Masanori",
  abstract     = "The purpose of this study is to introduce new design-criteria
                  for next-generation hyperparameter optimization software. The
                  criteria we propose include (1) define-by-run API that allows
                  users to construct the parameter search space dynamically,
                  (2) efficient implementation of both searching and pruning
                  strategies, and (3) easy-to-setup, versatile architecture
                  that can be deployed for various purposes, ranging from
                  scalable distributed computing to light-weight experiment
                  conducted via interactive interface. In order to prove our
                  point, we will introduce Optuna, an optimization software
                  which is a culmination of our effort in the development of a
                  next generation optimization software. As an optimization
                  software designed with define-by-run principle, Optuna is
                  particularly the first of its kind. We will present the
                  design-techniques that became necessary in the development of
                  the software that meets the above criteria, and demonstrate
                  the power of our new design through experimental results and
                  real world applications. Our software is available under the
                  MIT license (https://github.com/pfnet/optuna/).",
  year         =  2019,
  primaryClass = "cs.LG",
  eprint       = "1907.10902"
}
@article{tibshirani1996regression,
  title={Regression shrinkage and selection via the lasso},
  author={Tibshirani, Robert},
  journal={Journal of the Royal Statistical Society: Series B (Methodological)},
  volume={58},
  number={1},
  pages={267--288},
  year={1996},
  publisher={Wiley Online Library}
}

@ARTICLE{Stekhoven2012-rt,
  title     = "{MissForest--non-parametric} missing value imputation for
               mixed-type data",
  author    = "Stekhoven, Daniel J and B{\"u}hlmann, Peter",
  abstract  = "MOTIVATION: Modern data acquisition based on high-throughput
               technology is often facing the problem of missing data.
               Algorithms commonly used in the analysis of such large-scale
               data often depend on a complete set. Missing value imputation
               offers a solution to this problem. However, the majority of
               available imputation methods are restricted to one type of
               variable only: continuous or categorical. For mixed-type data,
               the different types are usually handled separately. Therefore,
               these methods ignore possible relations between variable types.
               We propose a non-parametric method which can cope with different
               types of variables simultaneously. RESULTS: We compare several
               state of the art methods for the imputation of missing values.
               We propose and evaluate an iterative imputation method
               (missForest) based on a random forest. By averaging over many
               unpruned classification or regression trees, random forest
               intrinsically constitutes a multiple imputation scheme. Using
               the built-in out-of-bag error estimates of random forest, we are
               able to estimate the imputation error without the need of a test
               set. Evaluation is performed on multiple datasets coming from a
               diverse selection of biological fields with artificially
               introduced missing values ranging from 10\% to 30\%. We show
               that missForest can successfully handle missing values,
               particularly in datasets including different types of variables.
               In our comparative study, missForest outperforms other methods
               of imputation especially in data settings where complex
               interactions and non-linear relations are suspected. The
               out-of-bag imputation error estimates of missForest prove to be
               adequate in all settings. Additionally, missForest exhibits
               attractive computational efficiency and can cope with
               high-dimensional data. AVAILABILITY: The package missForest is
               freely available from http://stat.ethz.ch/CRAN/. CONTACT:
               stekhoven@stat.math.ethz.ch; buhlmann@stat.math.ethz.ch",
  journal   = "Bioinformatics",
  publisher = "Oxford University Press (OUP)",
  volume    =  28,
  number    =  1,
  pages     = "112--118",
  month     =  jan,
  year      =  2012,
  language  = "en"
}

@article{Cigdem2024RA,
    author = {Cigdem, Ozkan and Chen, Shengjia and Zhang, Chaojie and Cho, Kyunghyun and Kijowski, Richard and Deniz, Cem M},
    title = {Estimating Time-to-Total Knee Replacement on Radiographs and MRI: A Multi-Modal Approach Using Self-Supervised Deep Learning},
    journal = {Radiology Advances},
    pages = {umae030},
    year = {2024},
    month = {11},
    issn = {2976-9337},
    doi = {10.1093/radadv/umae030},
    url = {https://doi.org/10.1093/radadv/umae030},
    eprint = {https://academic.oup.com/radadv/advance-article-pdf/doi/10.1093/radadv/umae030/60686238/umae030.pdf},
}
@inproceedings{Cigdem2024_ISMRM,
  title = "Estimation of Time-to-Total Knee Replacement Surgery with Deep Learning using MRI and Clinical Data",
  author = "Ozkan Cigdem and Eisa Hedayati and Haresh Rajamohand and Kyunghyun Cho and Gregory Chang and Richard Kijowski and Cem M Deniz",
  booktitle = "Proceedings of the 2024 ISMRM \& ISMRT Annual Meeting \& Exhibition",
  year = 2024,
  address = "Singapore"
}