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   Author="Weingartner, S.  and Messner, N. M.  and Budjan, J.  and Lossnitzer, D.  and Mattler, U.  and Papavassiliu, T.  and Zollner, F. G.  and Schad, L. R. ",
   Title="{{M}yocardial {T}1-mapping at 3{T} using saturation-recovery: reference values, precision and comparison with {M}{O}{L}{L}{I}}",
   Journal="J Cardiovasc Magn Reson",
   Year="2016",
   Volume="18",
   Number="1",
   Pages="84",
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}
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   Author="Weingartner, S.  and Akcakaya, M.  and Basha, T.  and Kissinger, K.  and Goddu, B.  and Berg, S.  and Manning, W.  and Nezafat, R. ",
   Title="{{C}ombined saturation/inversion recovery sequences for improved evaluation of scar and diffuse fibrosis in patients with arrhythmia or heart rate variability}",
   Journal="Magn Reson Med",
   Year="2014",
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  title={PARAFAC. Tutorial and applications},
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}
@inproceedings{Trzasko,
	title={A unified tensor regression framework for calibrationless dynamic, multi-channel MRI reconstruction},
	author={Trzasko, Joshua and Manduca, Armando},
	booktitle={Proc. ISMRM},
	pages={603},
	year={2013}
}
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	Author="He, J.  and Liu, Q.  and Christodoulou, A. G.  and Ma, C.  and Lam, F.  and Liang, Z. P. ",
	Title="{{A}ccelerated {H}igh-{D}imensional {M}{R} {I}maging {W}ith {S}parse {S}ampling {U}sing {L}ow-{R}ank {T}ensors}",
	Journal="IEEE Trans Med Imaging",
	Year="2016",
	Volume="35",
	Number="9",
	Pages="2119--2129"
}
@inproceedings{Kargas,
	title={Low-Rank Tensor Regularization for Improved Dynamic Quantitative Magnetic Resonance Imaging},
	author={Kargas, Nikolaos and Weing{\"a}rtner, Sebastian and Sidiropoulos, Nicholas D and Ak{\c{c}}akaya, Mehmet},
	year={2017},
	booktitle={SPARS}
}
@inproceedings{Mardani,
  title={Accelerating dynamic MRI via tensor subspace learning},
  author={Mardani, M and Ying, L and Giannakis, GB},
  booktitle={Proc. ISMRM},
  pages={3808},
  year={2015}
}
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   Title="{{M}ultidimensional compressed sensing {M}{R}{I} using tensor decomposition-based sparsifying transform}",
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   Year="2014",
   Volume="9",
   Number="6",
   Pages="e98441"
}

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	Title="{{T}emporally resolved parametric assessment of {Z}-magnetization recovery ({T}{O}{P}{A}{Z}): {D}ynamic myocardial {T}1 mapping using a cine steady-state look-locker approach}",
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	title={Tensor decomposition for signal processing and machine learning},
	author={Sidiropoulos, Nicholas D and De Lathauwer, Lieven and Fu, Xiao and Huang, Kejun and Papalexakis, Evangelos E and Faloutsos, Christos},
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	title={Tensor decompositions and applications},
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}
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}
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	title={Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices},
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}
@inproceedings{Terzopoulos,
	title={Multilinear analysis of image ensembles: Tensorfaces},
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}
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	title={Locally Low-Rank tensor regularization for high-resolution quantitative dynamic {MRI}},
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}
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	Title="{{P}ost-infarction ventricular arrhythmias originating in papillary muscles}",
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	Title="{{X}{D}-{G}{R}{A}{S}{P}: {G}olden-angle radial {M}{R}{I} with reconstruction of extra motion-state dimensions using compressed sensing}",
	Journal="Magn Reson Med",
	Year="2016",
	Volume="75",
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	Month="Feb"
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	Title="{{M}agnetic resonance multitasking for motion-resolved quantitative cardiovascular imaging}",
	Journal="Nat Biomed Eng",
	Year="2018",
	Volume="2",
	Pages="215--226"
}
@inproceedings{huang2017densely,
  title={Densely connected convolutional networks},
  Author={Huang, G. and Liu, Z. and Van Der Maaten, L. and Weinberger, K. Q.},
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}

@article{shi_review_2020,
	title = {Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for {COVID}-19},
	year={2020},
 	url = {http://arxiv.org/abs/2004.02731},
	abstract = {(This paper was submitted as an invited paper to {IEEE} Reviews in Biomedical Engineering on April 6, 2020.) The pandemic of coronavirus disease 2019 ({COVID}-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography ({CT}) plays an essential role in the global fight against {COVID}-19, whereas the recently emerging artificial intelligence ({AI}) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by {AI}) toward {COVID}-19. For example, {AI}-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, {AI} can improve work efficiency by accurate delination of infections in X-ray and {CT} images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with {COVID}-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of {AI} with X-ray and {CT}, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against {COVID}-19.},
	journaltitle = {{arXiv}:2004.02731 [cs, eess, q-bio]},
	author = {Shi, Feng and Wang, Jun and Shi, Jun and Wu, Ziyan and Wang, Qian and Tang, Zhenyu and He, Kelei and Shi, Yinghuan and Shen, Dinggang},
 	urldate = {2020-04-08},
	date = {2020-04-07},
	eprinttype = {arxiv},
	eprint = {2004.02731},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods},
	file = {arXiv Fulltext PDF:C\:\\Users\\josep\\Zotero\\storage\\SWSK77XD\\Shi et al. - 2020 - Review of Artificial Intelligence Techniques in Im.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\josep\\Zotero\\storage\\JD2CZZDL\\2004.html:text/html},
	journal={arXiv preprint arXiv:2004.0273}
}

@article{dong_interactive_2020,
	title = {An interactive web-based dashboard to track {COVID}-19 in real time},
	volume = {20},
	issn = {14733099},
 	url = {https://linkinghub.elsevier.com/retrieve/pii/S1473309920301201},
	doi = {10.1016/S1473-3099(20)30120-1},
	pages = {533--534},
	year={2020},
	number = {5},
	journal = {The Lancet Infectious Diseases},
	shortjournal = {The Lancet Infectious Diseases},
	author = {Dong, Ensheng and Du, Hongru and Gardner, Lauren},
 	urldate = {2020-06-14},
	date = {2020-05},
	langid = {english},
	file = {Dong et al. - 2020 - An interactive web-based dashboard to track COVID-.pdf:C\:\\Users\\josep\\Zotero\\storage\\9XGVWQ5Z\\Dong et al. - 2020 - An interactive web-based dashboard to track COVID-.pdf:application/pdf}
}

@article{hui_clinical_2020,
	title = {Clinical utility of chest radiography for severe {COVID}-19},
	volume = {10},
	issn = {2223-4292},
 	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7358410/},
	year={2020},
	doi = {10.21037/qims-20-642},
	abstract = {Background
Chest radiography ({CXR}) is performed more widely and readily than {CT} for the management of coronavirus disease ({COVID}-19), but there remains little data on its clinical utility. This study aims to assess the diagnostic performance of {CXR}, with emphasis on its predictive value, for severe {COVID}-19 disease.

Methods
A retrospective cohort study was conducted, 358 chest radiographs were performed on 109 {COVID}-19 patients (median age 44.4 years, 58 males and 30 with comorbidities) admitted between 22 January 2020 and 15 March 2020. Each {CXR} was reviewed and scored by three radiologists in consensus using a 72-point {COVID}-19 Radiographic Score ({CRS}). Disease severity was determined by the need for supplemental oxygen and mechanical ventilation.

Results
Patients who needed supplemental oxygen (n=19, 17.4\%) were significantly older (P{\textless}0.001) and significantly more of them had co-morbidities (P=0.011). They also had higher C-reactive protein ({CRP}) (P{\textless}0.001), higher lactate dehydrogenase ({LDH}) (P{\textless}0.001), lower lymphocyte count (P{\textless}0.001) and lower hemoglobin (Hb) (P=0.001). Their initial ({CRSinitial}) and maximal {CRS} ({CRSmax}) were higher (P{\textless}0.001). Adjusting for age and baseline hemoglobin, the {AUROC} of {CRSmax} (0.983) was as high as {CRPmax} (0.987) and higher than the {AUROC} for lymphocyte countmin (0.897), and {LDHmax} (0.900). The {AUROC} for {CRSinitial} was slightly lower (0.930). {CRSinitial} ≥5 had a sensitivity of 63\% and specificity of 92\% in predicting the need for oxygen, and 73\% sensitivity and 88\% specificity in predicting the need for mechanical ventilation. {CRS} between the 6th and 10th day from the onset of symptoms ({CRSD}6-10) ≥5 had a sensitivity of 89\% and specificity of 95\% in predicting the need for oxygen, and 100\% sensitivity and 86\% specificity in predicting the need for mechanical ventilation.

Conclusions
Adjusting for key confounders of age and baseline Hb, {CRSmax} performed comparable to or better than laboratory markers in the diagnosis of severe disease. {CXR} performed between the 6th and 10th days from symptom onset was a better predictor of severe disease than {CXR} performed earlier at presentation. A benign clinical course was seen in {CXR} that were normal or had very mild abnormalities.},
	pages = {1540--1550},
	number = {7},
	journal = {Quantitative Imaging in Medicine and Surgery},
	shortjournal = {Quant Imaging Med Surg},
	author = {Hui, Terrence C. H. and Khoo, Hau Wei and Young, Barnaby E. and Haja Mohideen, Salahudeen Mohamed and Lee, Yeong Shyan and Lim, Chien Joo and Leo, Yee Sin and Kaw, Gregory J. L. and Lye, David C. and Tan, Cher Heng},
 	urldate = {2020-10-16},
	date = {2020-07},
	pmid = {32676371},
	pmcid = {PMC7358410},
	file = {PubMed Central Full Text PDF:C\:\\Users\\josep\\Zotero\\storage\\GTFL4PN2\\Hui et al. - 2020 - Clinical utility of chest radiography for severe C.pdf:application/pdf}
}

@article{toussie_clinical_2020,
	title = {Clinical and Chest Radiography Features Determine Patient Outcomes in                     Young and Middle-aged Adults with {COVID}-19},
	volume = {297},
	issn = {0033-8419},
	year={2020},
 	url = {https://pubs.rsna.org/doi/10.1148/radiol.2020201754},
	doi = {10.1148/radiol.2020201754},
	abstract = {{BackgroundChest} radiography has not been validated for its prognostic utility in                             evaluating patients with coronavirus disease 2019 ({COVID}-19).{PurposeTo} analyze the prognostic value of a chest radiograph severity scoring                             system for younger (nonelderly) patients with {COVID}-19 at initial                             presentation to the emergency department ({ED}); outcomes of interest                             included hospitalization, intubation, prolonged stay, sepsis, and                             death.Materials and {MethodsIn} this retrospective study, patients between the ages of 21 and 50 years                             who presented to the {ED} of an urban multicenter health system from March                             10 to March 26, 2020, with {COVID}-19 confirmation on real-time reverse                             transcriptase polymerase chain reaction were identified. Each                             patient’s {ED} chest radiograph was divided into six zones and                             examined for opacities by two cardiothoracic radiologists, and scores                             were collated into a total concordant lung zone severity score. Clinical                             and laboratory variables were collected. Multivariable logistic                             regression was used to evaluate the relationship between clinical                             parameters, chest radiograph scores, and patient outcomes.{ResultsThe} study included 338 patients: 210 men (62\%), with median age of 39                             years (interquartile range, 31–45 years). After adjustment for                             demographics and comorbidities, independent predictors of hospital                             admission (n = 145, 43\%) were chest radiograph                             severity score of 2 or more (odds ratio, 6.2; 95\% confidence interval                             [{CI}]: 3.5, 11; P {\textless} .001) and obesity (odds ratio,                             2.4 [95\% {CI}: 1.1, 5.4] or morbid obesity). Among patients who were                             admitted, a chest radiograph score of 3 or more was an independent                             predictor of intubation (n = 28) (odds ratio, 4.7;                             95\% {CI}: 1.8, 13; P = .002) as was hospital site.                             No significant difference was found in primary outcomes across race and                             ethnicity or those with a history of tobacco use, asthma, or diabetes                             mellitus type {II}.{ConclusionFor} patients aged 21–50 years with coronavirus disease 2019                             presenting to the emergency department, a chest radiograph severity                             score was predictive of risk for hospital admission and intubation.© {RSNA}, 2020Online supplemental material is available for this                                     article.},
	pages = {E197--E206},
	number = {1},
	journal = {Radiology},
	shortjournal = {Radiology},
	author = {Toussie, Danielle and Voutsinas, Nicholas and Finkelstein, Mark and Cedillo, Mario                             A. and Manna, Sayan and Maron, Samuel                             Z. and Jacobi, Adam and Chung, Michael and Bernheim, Adam and Eber, Corey and Concepcion, Jose and Fayad, Zahi                         A. and Gupta, Yogesh                             Sean},
 	urldate = {2020-10-16},
	date = {2020-05-14},
	note = {Publisher: Radiological Society of North America},
	file = {Full Text PDF:C\:\\Users\\josep\\Zotero\\storage\\RNEJUZNM\\Toussie et al. - 2020 - Clinical and Chest Radiography Features Determine .pdf:application/pdf;Snapshot:C\:\\Users\\josep\\Zotero\\storage\\G7Z7JNNB\\radiol.html:text/html}
}

@article{ziehr_respiratory_2020,
	title = {Respiratory Pathophysiology of Mechanically Ventilated Patients with {COVID}-19: A Cohort Study},
	volume = {201},
	issn = {1073-449X},
 	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7301734/},
	doi = {10.1164/rccm.202004-1163LE},
	shorttitle = {Respiratory Pathophysiology of Mechanically Ventilated Patients with {COVID}-19},
	pages = {1560--1564},
	number = {12},
	journaltitle = {American Journal of Respiratory and Critical Care Medicine},
	shortjournal = {Am J Respir Crit Care Med},
	author = {Ziehr, David R. and Alladina, Jehan and Petri, Camille R. and Maley, Jason H. and Moskowitz, Ari and Medoff, Benjamin D. and Hibbert, Kathryn A. and Thompson, B. Taylor and Hardin, C. Corey},
 	urldate = {2020-10-16},
	date = {2020-06-15},
	pmid = {32348678},
	pmcid = {PMC7301734},
	file = {PubMed Central Full Text PDF:C\:\\Users\\josep\\Zotero\\storage\\N99MLRB7\\Ziehr et al. - 2020 - Respiratory Pathophysiology of Mechanically Ventil.pdf:application/pdf}
}

@article{pan_lung_2020,
	title = {Lung Recruitability in {COVID}-19–associated Acute Respiratory Distress Syndrome: A Single-Center Observational Study},
	volume = {201},
	issn = {1073-449X},
 	url = {https://www.atsjournals.org/doi/10.1164/rccm.202003-0527LE},
	doi = {10.1164/rccm.202003-0527LE},
	shorttitle = {Lung Recruitability in {COVID}-19–associated Acute Respiratory Distress Syndrome},
	pages = {1294--1297},
	number = {10},
	journaltitle = {American Journal of Respiratory and Critical Care Medicine},
	shortjournal = {Am J Respir Crit Care Med},
	author = {Pan, Chun and Chen, Lu and Lu, Cong and Zhang, Wei and Xia, Jia-An and Sklar, Michael C. and Du, Bin and Brochard, Laurent and Qiu, Haibo},
 	urldate = {2020-10-16},
	date = {2020-03-23},
	note = {Publisher: American Thoracic Society - {AJRCCM}},
	file = {Full Text PDF:C\:\\Users\\josep\\Zotero\\storage\\W294YHI6\\Pan et al. - 2020 - Lung Recruitability in COVID-19–associated Acute R.pdf:application/pdf;Snapshot:C\:\\Users\\josep\\Zotero\\storage\\3MVTIYEH\\rccm.html:text/html}
}

@article{guerin_prone_2013,
	title = {Prone Positioning in Severe Acute Respiratory Distress Syndrome},
	volume = {368},
	issn = {0028-4793},
 	url = {https://doi.org/10.1056/NEJMoa1214103},
	doi = {10.1056/NEJMoa1214103},
	year={2013},
	abstract = {Prone positioning has been used for many years to improve oxygenation in patients who require mechanical ventilatory support for management of the acute respiratory distress syndrome ({ARDS}). Randomized, controlled trials have confirmed that oxygenation is significantly better when patients are in the prone position than when they are in the supine position.1,2 Furthermore, several lines of evidence have shown that prone positioning could prevent ventilator-induced lung injury.3–6 In several previous trials, these physiological benefits did not translate into better patient outcomes, since no significant improvement was observed in patient survival with prone positioning.7–10 However, meta-analyses2,11 have . . .},
	pages = {2159--2168},
	number = {23},
	journal = {New England Journal of Medicine},
	author = {Guérin, Claude and Reignier, Jean and Richard, Jean-Christophe and Beuret, Pascal and Gacouin, Arnaud and Boulain, Thierry and Mercier, Emmanuelle and Badet, Michel and Mercat, Alain and Baudin, Olivier and Clavel, Marc and Chatellier, Delphine and Jaber, Samir and Rosselli, Sylvène and Mancebo, Jordi and Sirodot, Michel and Hilbert, Gilles and Bengler, Christian and Richecoeur, Jack and Gainnier, Marc and Bayle, Frédérique and Bourdin, Gael and Leray, Véronique and Girard, Raphaele and Baboi, Loredana and Ayzac, Louis},
 	urldate = {2020-10-16},
	date = {2013-06-06},
	pmid = {23688302},
	Publisher = {Massachusetts Medical Society}
}

@article{wu_mobile_2020,
	title = {Mobile X-rays are highly valuable for critically ill {COVID} patients},
	volume = {30},
	issn = {1432-1084},
 	url = {https://doi.org/10.1007/s00330-020-06918-2},
	doi = {10.1007/s00330-020-06918-2},
	pages = {5217--5219},
	number = {9},
	journaltitle = {European Radiology},
	shortjournal = {Eur Radiol},
	author = {Wu, Gang and Li, Xiaoming},
 	urldate = {2020-10-16},
	date = {2020-09-01},
	langid = {english},
	file = {Springer Full Text PDF:C\:\\Users\\josep\\Zotero\\storage\\R6I6TWI7\\Wu and Li - 2020 - Mobile X-rays are highly valuable for critically i.pdf:application/pdf}
}

@article{marini_management_2020,
	title = {Management of {COVID}-19 Respiratory Distress},
	volume = {323},
	issn = {0098-7484},
 	url = {https://jamanetwork.com/journals/jama/fullarticle/2765302},
	doi = {10.1001/jama.2020.6825},
	pages = {2329},
	number = {22},
	journaltitle = {{JAMA}},
	shortjournal = {{JAMA}},
	author = {Marini, John J. and Gattinoni, Luciano},
 	urldate = {2020-10-16},
	date = {2020-06-09},
	langid = {english},
	file = {Full Text:C\:\\Users\\josep\\Zotero\\storage\\3MT54GZS\\Marini and Gattinoni - 2020 - Management of COVID-19 Respiratory Distress.pdf:application/pdf}
}

@article{bellani_epidemiology_2016,
	title = {Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries},
	volume = {315},
	issn = {0098-7484},
 	url = {http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.2016.0291},
	doi = {10.1001/jama.2016.0291},
	pages = {788},
	number = {8},
	journaltitle = {{JAMA}},
	shortjournal = {{JAMA}},
	author = {Bellani, Giacomo and Laffey, John G. and Pham, Tài and Fan, Eddy and Brochard, Laurent and Esteban, Andres and Gattinoni, Luciano and van Haren, Frank and Larsson, Anders and McAuley, Daniel F. and Ranieri, Marco and Rubenfeld, Gordon and Thompson, B. Taylor and Wrigge, Hermann and Slutsky, Arthur S. and Pesenti, Antonio and {for the LUNG SAFE Investigators and the ESICM Trials Group}},
 	urldate = {2020-10-16},
	date = {2016-02-23},
	langid = {english},
	file = {Full Text:C\:\\Users\\josep\\Zotero\\storage\\MLK6Q676\\Bellani et al. - 2016 - Epidemiology, Patterns of Care, and Mortality for .pdf:application/pdf}
}

@article{bae_predicting_2020,
	title = {Predicting Mechanical Ventilation Requirement and Mortality in {COVID}-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study},
 	url = {http://arxiv.org/abs/2007.08028},
	shorttitle = {Predicting Mechanical Ventilation Requirement and Mortality in {COVID}-19 using Radiomics and Deep Learning on Chest Radiographs},
	abstract = {Objectives: To predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs ({CXR}) for coronavirus disease 2019 ({COVID}-19) patients. We also investigate the relative advantages of deep learning ({DL}), radiomics, and {DL} of radiomic-embedded feature maps in predicting these outcomes. Methods: This two-center, retrospective study analyzed deidentified {CXRs} taken from 514 patients suspected of {COVID}-19 infection on presentation at Stony Brook University Hospital ({SBUH}) and Newark Beth Israel Medical Center ({NBIMC}) between the months of March and June 2020. A {DL} segmentation pipeline was developed to generate masks for both lung fields and artifacts for each {CXR}. Machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated on 353 baseline {CXRs} taken from {COVID}-19 positive patients. A novel radiomic embedding framework is also explored for outcome prediction. Results: Classification models for mechanical ventilation requirement (test N=154) and mortality (test N=190) had {AUCs} of up to 0.904 and 0.936, respectively. We also found that the inclusion of radiomic-embedded maps improved {DL} model predictions of clinical outcomes. Conclusions: We demonstrate the potential for computerized analysis of baseline {CXR} in predicting disease outcomes in {COVID}-19 patients. Our results also suggest that radiomic embedding improves {DL} models in medical image analysis, a technique that might be explored further in other pathologies. The models proposed in this study and the prognostic information they provide, complementary to other clinical data, might be used to aid physician decision making and resource allocation during the {COVID}-19 pandemic.},
	journaltitle = {{arXiv}:2007.08028 [cs, eess, q-bio]},
	author = {Bae, Joseph and Kapse, Saarthak and Singh, Gagandeep and Phatak, Tej and Green, Jeremy and Madan, Nikhil and Prasanna, Prateek},
 	urldate = {2020-10-16},
	year = {2020},
	eprinttype = {arxiv},
	eprint = {2007.08028},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, I.2.6, J.3, Quantitative Biology - Quantitative Methods},
	file = {arXiv Fulltext PDF:C\:\\Users\\josep\\Zotero\\storage\\VI55S5LC\\Bae et al. - 2020 - Predicting Mechanical Ventilation Requirement and .pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\josep\\Zotero\\storage\\Q54BIRRW\\2007.html:text/html},
	journal={arXiv preprint arXiv:2007.08028}
}

@article{wong_frequency_2020,
	title = {Frequency and Distribution of Chest Radiographic Findings in {COVID}-19                     Positive Patients},
	issn = {0033-8419},
 	url = {https://pubs.rsna.org/doi/10.1148/radiol.2020201160},
	doi = {10.1148/radiol.2020201160},
	abstract = {{BackgroundCurrent} {COVID}-19 radiological literature is dominated by {CT} and a                             detailed description of chest x-ray ({CXR}) appearances in relation to the                             disease time course is lacking.{PurposeTo} describe the time course and severity of the {CXR} findings of {COVID}-19                             and correlate these with real time reverse transcription polymerase                             chain reaction ({RT}-{PCR}) testing for {SARS}-Cov-2 nucleic acid.Materials and {MethodsRetrospective} study of {COVID}-19 patients with {RT}-{PCR} confirmation and                             {CXRs} admitted across 4 hospitals evaluated between January and March                             2020. Baseline and serial {CXRs} (total 255 {CXRs}) were reviewed along with                             {RT}-{PCRs}. Correlation with concurrent {CTs} (total 28 {CTs}) was made when                             available. Two radiologists scored each {CXR} in consensus for:                             consolidation, ground glass opacity ({GGO}), location and pleural fluid. A                             severity index was determined for each lung. The lung scores were summed                             to produce the final severity score.{ResultsThere} were 64 patients (26 men, mean age 56±19 years). Of these, 58,                             44 and 38 patients had positive initial {RT}-{PCR} (91\%, [{CI}: 81-96\%]),                             abnormal baseline {CXR} (69\%, [{CI}: 56-80\%]) and positive initial {RT}-{PCR}                             with abnormal baseline {CXR} (59 [{CI}:46-71\%]) respectively. Six patients                             (9\%) showed {CXR} abnormalities before eventually testing positive on                             {RT}-{PCR}. Sensitivity of initial {RT}-{PCR} (91\% [95\% {CI}: 83-97\%]) was higher                             than baseline {CXR} (69\% [95\% {CI}: 56-80\%]) (p =                             0.009). Radiographic (mean 6 ± 5 days) and virologic recovery (mean                             8 ± 6 days) were not significantly different                             (p= 0.33). Consolidation was the most common                             finding (30/64, 47\%), followed by {GGO} (21/64, 33\%). {CXR} abnormalities                             had a peripheral (26/64, 41\%) and lower zone distribution (32/64, 50\%)                             with bilateral involvement (32/64, 50\%). Pleural effusion was uncommon                             (2/64, 3\%). The severity of {CXR} findings peaked at 10-12 days from the                             date of symptom onset.{ConclusionChest} x-ray findings in {COVID}-19 patients frequently showed bilateral                             lower zone consolidation which peaked at 10-12 days from symptom                             onset.},
	pages = {201160},
	journaltitle = {Radiology},
	shortjournal = {Radiology},
	author = {Wong, Ho Yuen Frank and Lam, Hiu Yin Sonia and Fong, Ambrose Ho-Tung and Leung, Siu                         Ting and Chin, Thomas Wing-Yan and Lo, Christine Shing Yen and Lui, Macy Mei-Sze and Lee, Jonan Chun Yin and Chiu, Keith Wan-Hang and Chung, Tom and Lee, Elaine Yuen Phin and Wan, Eric Yuk Fai and Hung, Fan Ngai Ivan and Lam, Tina Poy Wing and Kuo, Michael and Ng, Ming-Yen},
 	urldate = {2020-06-04},
	date = {2020-03-27},
	note = {Publisher: Radiological Society of North America},
	file = {Full Text PDF:C\:\\Users\\josep\\Zotero\\storage\\7GNEA68H\\Wong et al. - 2020 - Frequency and Distribution of Chest Radiographic F.pdf:application/pdf;Snapshot:C\:\\Users\\josep\\Zotero\\storage\\AWQFM7XW\\radiol.html:text/html}
}

@misc{noauthor_acr_nodate,
    author={{American College of Radiology}},
	title = {{ACR} Recommendations for the use of Chest Radiography and Computed Tomography ({CT}) for Suspected {COVID}-19 Infection},
	url = {https://www.acr.org/Advocacy-and-Economics/ACR-Position-Statements/Recommendations-for-Chest-Radiography-and-CT-for-Suspected-COVID19-Infection},
	abstract = {As {COVID}-19 spreads in the U.S., there is growing interest in the role and appropriateness of chest radiographs ({CXR}) and computed tomography ({CT}) for the screening, diagnosis and management of patients with suspected or known {COVID}-19 infection.},
	year = {2020},
	urldate = {2020-06-15},
	note = {Library Catalog: www.acr.org},
	file = {Snapshot:C\:\\Users\\josep\\Zotero\\storage\\5ZW3EWNK\\Recommendations-for-Chest-Radiography-and-CT-for-Suspected-COVID19-Infection.html:text/html}
}

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@inproceedings{lao_leveraging_2018,
	title = {Leveraging Disease Progression Learning for Medical Image Recognition},
	isbn = {978-1-5386-5488-0},
    url = {https://www.computer.org/csdl/proceedings-article/bibm/2018/08621245/17D45X0yjUK},
	doi = {10.1109/BIBM.2018.8621245},
	language = {English},
	urldate = {2021-03-18},
	publisher = {IEEE Computer Society},
	author = {Lao, Qicheng and Fevens, Thomas and Wang, Boyu},
	month = dec,
	year = {2018},
	pages = {671--675},
	file = {Snapshot:C\:\\Users\\josep\\Zotero\\storage\\ISUVVJA5\\17D45X0yjUK.html:text/html;Submitted Version:C\:\\Users\\josep\\Zotero\\storage\\J9GWLH94\\Lao et al. - 2018 - Leveraging Disease Progression Learning for Medica.pdf:application/pdf}
}

@article{jiang_predicting_2018,
	title = {Predicting the progression of ophthalmic disease based on slit-lamp images using a deep temporal sequence network},
	volume = {13},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0201142},
	language = {eng},
	number = {7},
	journal = {PloS One},
	author = {Jiang, Jiewei and Liu, Xiyang and Liu, Lin and Wang, Shuai and Long, Erping and Yang, Haoqing and Yuan, Fuqiang and Yu, Deying and Zhang, Kai and Wang, Liming and Liu, Zhenzhen and Wang, Dongni and Xi, Changzun and Lin, Zhuoling and Wu, Xiaohang and Cui, Jiangtao and Zhu, Mingmin and Lin, Haotian},
	year = {2018},
	pmid = {30063738},
	pmcid = {PMC6067742},
	keywords = {Diagnosis, Computer-Assisted, Disease Progression, Eye Diseases, Follow-Up Studies, Humans, Neural Networks, Computer, Pattern Recognition, Automated, Prognosis, Sensitivity and Specificity, Slit Lamp},
	pages = {e0201142},
	file = {Full Text:C\:\\Users\\josep\\Zotero\\storage\\PTBVNITS\\Jiang et al. - 2018 - Predicting the progression of ophthalmic disease b.pdf:application/pdf}
}

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	issn = {2473-4209},
	doi = {10.1002/mp.13765},
	language = {eng},
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	month = oct,
	year = {2019},
	pmid = {31410855},
	pmcid = {PMC7391789},
	keywords = {deep learning, Deep Learning, Humans, longitudinal, lung, Lung Neoplasms, Magnetic Resonance Imaging, MRI, radiotherapy, Retrospective Studies, treatment response},
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@inproceedings{cowan2021evolution,
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  pages={1159709},
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  organization={International Society for Optics and Photonics}
}



@article{wang_predictive_2018,
	title = {Predictive {Modeling} of the {Progression} of {Alzheimer}’s {Disease} with {Recurrent} {Neural} {Networks}},
	volume = {8},
	copyright = {2018 The Author(s)},
	issn = {2045-2322},
 	url = {https://www.nature.com/articles/s41598-018-27337-w},
	doi = {10.1038/s41598-018-27337-w},
	language = {en},
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 	urldate = {2021-03-18},
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Publisher: Nature Publishing Group},
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}




