
@misc{noauthor_imagenet_nodate,
	title = {{ImageNet}: {A} large-scale hierarchical image database {\textbar} {IEEE} {Conference} {Publication} {\textbar} {IEEE} {Xplore}},
	url = {https://ieeexplore.ieee.org/document/5206848},
	urldate = {2025-11-23},
}

@inproceedings{touvron_training_2021,
	title = {Training data-efficient image transformers \& distillation through attention},
	url = {https://proceedings.mlr.press/v139/touvron21a.html},
	abstract = {Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. These high-performing vision transformers are pre-trained with hundreds of millions of images using a large infrastructure, thereby limiting their adoption. In this work, we produce competitive convolution-free transformers trained on ImageNet only using a single computer in less than 3 days. Our reference vision transformer (86M parameters) achieves top-1 accuracy of 83.1\% (single-crop) on ImageNet with no external data. We also introduce a teacher-student strategy specific to transformers. It relies on a distillation token ensuring that the student learns from the teacher through attention, typically from a convnet teacher. The learned transformers are competitive (85.2\% top-1 acc.) with the state of the art on ImageNet, and similarly when transferred to other tasks. We will share our code and models.},
	language = {en},
	urldate = {2025-11-23},
	booktitle = {Proceedings of the 38th {International} {Conference} on {Machine} {Learning}},
	publisher = {PMLR},
	author = {Touvron, Hugo and Cord, Matthieu and Douze, Matthijs and Massa, Francisco and Sablayrolles, Alexandre and Jegou, Herve},
	month = jul,
	year = {2021},
	note = {ISSN: 2640-3498},
	pages = {10347--10357},
}

@article{howard_searching_2019,
	title = {Searching for {MobileNetV3}},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	url = {https://ieeexplore.ieee.org/document/9008835/},
	doi = {10.1109/ICCV.2019.00140},
	abstract = {We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2\% more accurate on ImageNet classification while reducing latency by 20\% compared to MobileNetV2. MobileNetV3-Small is 6.6\% more accurate compared to a MobileNetV2 model with comparable latency. MobileNetV3-Large detection is over 25\% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 34\% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation.},
	urldate = {2025-11-23},
	journal = {2019 IEEE/CVF International Conference on Computer Vision (ICCV)},
	author = {Howard, Andrew and Sandler, Mark and Chen, Bo and Wang, Weijun and Chen, Liang-Chieh and Tan, Mingxing and Chu, Grace and Vasudevan, Vijay and Zhu, Yukun and Pang, Ruoming and Adam, Hartwig and Le, Quoc},
	month = oct,
	year = {2019},
	note = {Conference Name: 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
ISBN: 9781728148038
Place: Seoul, Korea (South)
Publisher: IEEE},
	pages = {1314--1324},
}

@inproceedings{howard_searching_2019-1,
	title = {Searching for {MobileNetV3}},
	url = {https://openaccess.thecvf.com/content_ICCV_2019/html/Howard_Searching_for_MobileNetV3_ICCV_2019_paper.html},
	urldate = {2025-11-23},
	author = {Howard, Andrew and Sandler, Mark and Chu, Grace and Chen, Liang-Chieh and Chen, Bo and Tan, Mingxing and Wang, Weijun and Zhu, Yukun and Pang, Ruoming and Vasudevan, Vijay and Le, Quoc V. and Adam, Hartwig},
	year = {2019},
	pages = {1314--1324},
}

@inproceedings{tan_efficientnetv2_2021,
	title = {{EfficientNetV2}: {Smaller} {Models} and {Faster} {Training}},
	shorttitle = {{EfficientNetV2}},
	url = {https://proceedings.mlr.press/v139/tan21a.html},
	abstract = {This paper introduces EfficientNetV2, a new family of convolutional networks that have faster training speed and better parameter efficiency than previous models. To develop these models, we use a combination of training-aware neural architecture search and scaling, to jointly optimize training speed and parameter efficiency. The models were searched from the search space enriched with new ops such as Fused-MBConv. Our experiments show that EfficientNetV2 models train much faster than state-of-the-art models while being up to 6.8x smaller. Our training can be further sped up by progressively increasing the image size during training, but it often causes a drop in accuracy. To compensate for this accuracy drop, we propose an improved method of progressive learning, which adaptively adjusts regularization (e.g. data augmentation) along with image size. With progressive learning, our EfficientNetV2 significantly outperforms previous models on ImageNet and CIFAR/Cars/Flowers datasets. By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87.3\% top-1 accuracy on ImageNet ILSVRC2012, outperforming the recent ViT by 2.0\% accuracy while training 5x-11x faster using the same computing resources.},
	language = {en},
	urldate = {2025-11-23},
	booktitle = {Proceedings of the 38th {International} {Conference} on {Machine} {Learning}},
	publisher = {PMLR},
	author = {Tan, Mingxing and Le, Quoc},
	month = jul,
	year = {2021},
	note = {ISSN: 2640-3498},
	pages = {10096--10106},
}

@inproceedings{he_deep_2016,
	title = {Deep {Residual} {Learning} for {Image} {Recognition}},
	url = {https://ieeexplore.ieee.org/document/7780459},
	doi = {10.1109/CVPR.2016.90},
	abstract = {Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57\% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28\% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC \& COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.},
	urldate = {2025-11-23},
	booktitle = {2016 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
	month = jun,
	year = {2016},
	note = {ISSN: 1063-6919},
	keywords = {Complexity theory, Degradation, Image recognition, Image segmentation, Neural networks, Training, Visualization},
	pages = {770--778},
}

@article{kruger_realtime_2025,
	title = {Realtime detection of spontaneous circulation in humans during cardiopulmonary resuscitation using a continuous hands-free carotid {Doppler}: a pilot study},
	volume = {26},
	issn = {2666-5204},
	shorttitle = {Realtime detection of spontaneous circulation in humans during cardiopulmonary resuscitation using a continuous hands-free carotid {Doppler}},
	url = {https://www.sciencedirect.com/science/article/pii/S2666520425002176},
	doi = {10.1016/j.resplu.2025.101080},
	abstract = {Background
The resuscitation society calls for precision-guided cardiopulmonary resuscitation (CPR), as current methods are inaccurate and time-consuming. RescueDoppler, a novel hands-free Doppler ultrasound system, continuously measures carotid blood flow during CPR. This pilot study assessed its performance, safety, and ability to detect chest compression-generated blood flow, spontaneous circulation, and return of spontaneous circulation (ROSC).
Method
We investigated RescueDoppler in adult cardiac arrest patients at two centres, in-hospital (IHCA) and out-of-hospital (OHCA). The cardiac arrest team placed the RescueDoppler probe over the left common carotid artery with a self-adhesive patch, collecting blinded data during CPR. Data were later interpreted and time-synchronized with defibrillator data using custom MATLAB® software.
Results
RescueDoppler was used in 26 IHCA and 36 OHCA patients from October 2023 to September 2024. Carotid blood flow curves were analyzed in 36 patients and synchronized with defibrillator data in 30. The RescueDoppler identified blood flow velocities generated by chest compressions and detected spontaneous circulation during rhythm checks. ROSC was defined by the presence of systolic and diastolic blood flow. No adverse events were reported but there were 22 device deficiencies mostly related to the self-adhesive patch and connecting cable. The system is user-friendly and requires minimal training.
Conclusion
Real-time detection of blood flow in the carotid artery with hands-free Doppler ultrasound during CPR is safe and feasible, although the fastening patch and host unit need optimization. The RescueDoppler system detects spontaneous circulation and ROSC during rhythm checks and ongoing chest compressions. Further research is required to confirm clinical relevance.},
	urldate = {2025-11-18},
	journal = {Resuscitation Plus},
	author = {Krüger, Guro Mæhlum and Birkeli, Sunniva Gjerald and Hansen, Øystein Myrlund and Faldaas, Bjørn Ove and Norvik, Anders and Lund, Hedda Juni and Egil Hautois, Gregory Louis and Flage, John Helge and Urteaga, Jon and Hergum, Torbjørn and Torp, Hans and Skogvoll, Eirik and Ingul, Charlotte Björk},
	month = nov,
	year = {2025},
	keywords = {Advanced life support (ALS), Cardiopulmonary resuscitation (CPR), Carotid Doppler ultrasound, Chest compressions, Mechanical chest compression, Operator independent Doppler ultrasound, Pulse check, Return of spontaneous circulation (ROSC), Spontaneous circulation, Wearable Doppler ultrasound},
	pages = {101080},
}

@article{li_two-dimensional_2022,
	title = {Two-dimensional {ECG}-based cardiac arrhythmia classification using {DSE}-{ResNet}},
	volume = {12},
	copyright = {2022 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-022-18664-0},
	doi = {10.1038/s41598-022-18664-0},
	abstract = {Electrocardiogram (ECG) is mostly used for the clinical diagnosis of cardiac arrhythmia due to its simplicity, non-invasiveness, and reliability. Recently, many models based on the deep neural networks have been applied to the automatic classification of cardiac arrhythmia with great success. However, most models independently extract the internal features of each lead in the 12-lead ECG during the training phase, resulting in a lack of inter-lead features. Here, we propose a general model based on the two-dimensional ECG and ResNet with detached squeeze-and-excitation modules (DSE-ResNet) to realize the automatic classification of normal rhythm and 8 cardiac arrhythmias. The original 12-lead ECG is spliced into a two-dimensional plane like a grayscale picture. DSE-ResNet is used to simultaneously extract the internal and inter-lead features of the two-dimensional ECG. Furthermore, an orthogonal experiment method is used to optimize the hyper-parameters of DSE-ResNet and a multi-model voting strategy is used to improve classification performance. Experimental results based on the test set of China Physiological Signal Challenge 2018 (CPSC2018) show that our model has average \$\$F\_1= 0.817\$\$for classifying normal rhythm and 8 cardiac arrhythmias. Meanwhile, compared with the state-of-art model in CPSC2018, our model achieved the best \$\$F\_1\$\$in 2 sub-abnormal types. This shows that the model based on the two-dimensional ECG and DSE-ResNet has advantage in detecting some cardiac arrhythmias and has the potential to be used as an auxiliary tool to help doctors perform cardiac arrhythmias analysis.},
	language = {en},
	number = {1},
	urldate = {2025-09-08},
	journal = {Scientific Reports},
	author = {Li, Jiahao and Pang, Shao-peng and Xu, Fangzhou and Ji, Peng and Zhou, Shuwang and Shu, Minglei},
	month = aug,
	year = {2022},
	note = {Publisher: Nature Publishing Group},
	keywords = {Cardiology, Imaging techniques, Information technology},
	pages = {14485},
}

@article{kim_novel_2025,
	title = {A novel hybrid {CNN}-transformer model for arrhythmia detection without {R}-peak identification using stockwell transform},
	volume = {15},
	copyright = {2025 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-025-92582-9},
	doi = {10.1038/s41598-025-92582-9},
	abstract = {This study presents a novel hybrid deep learning model for arrhythmia classification from electrocardiogram signals, utilizing the stockwell transform for feature extraction. As ECG signals are time-series data, they are transformed into the frequency domain to extract relevant features. Subsequently, a CNN is employed to capture local patterns, while a transformer architecture learns long-term dependencies. Unlike traditional CNN-based models that require R-peak detection, the proposed model operates without it and demonstrates superior accuracy and efficiency. The findings contribute to enhancing the accuracy of ECG-based arrhythmia diagnosis and are applicable to real-time monitoring systems. Specifically, the model achieves an accuracy of 97.8\% on the Icentia11k dataset using four arrhythmia classes and 99.58\% on the MIT-BIH dataset using five arrhythmia classes.},
	language = {en},
	number = {1},
	urldate = {2025-09-08},
	journal = {Scientific Reports},
	author = {Kim, Donghyeon and Lee, Kyoung Ryul and Lim, Dong Seok and Lee, Kwang Hyun and Lee, Jong Seon and Kim, Dae-Yeol and Sohn, Chae-Bong},
	month = mar,
	year = {2025},
	note = {Publisher: Nature Publishing Group},
	keywords = {Arrhythmias, Cardiology, Computational biology and bioinformatics},
	pages = {7817},
}

@article{bai_hybrid_2024,
	title = {A hybrid deep learning network for automatic diagnosis of cardiac arrhythmia based on 12-lead {ECG}},
	volume = {14},
	copyright = {2024 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-024-75531-w},
	doi = {10.1038/s41598-024-75531-w},
	abstract = {Cardiac arrhythmias are the leading cause of death and pose a huge health and economic burden globally. Electrocardiography (ECG) is an effective technique for the diagnosis of cardiovascular diseases because of its noninvasive and cost-effective advantages. However, traditional ECG analysis relies heavily on the clinical experience of physicians, which can be challenging and time-consuming to produce valid diagnostic results. This work proposes a new hybrid deep learning model that combines convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) with multi-head attention (CBGM model). Specifically, the model consists of seven convolutional layers with varying filter sizes (4, 16, 32, and 64) and three pooling layers, respectively, while the BiGRU module includes two layers with 64 units each followed by multi-head attention (8-heads). The combination of CNN and BiGRU effectively captures spatio-temporal features of ECG signals, with multi-head attention comprehensively extracted global correlations among multiple segments of ECG signals. The validation in the MIT-BIH arrhythmia database achieved an accuracy of 99.41\%, a precision of 99.15\%, a specificity of 99.68\%, and an F1-Score of 99.21\%, indicating its robust performance across different evaluation metrics. Additionally, the model’s performance was evaluated on the PTB Diagnostic ECG Database, where it achieved an accuracy of 98.82\%, demonstrating its generalization capability. Comparative analysis against previous methods revealed that our proposed CBGM model exhibits more higher performance in automatic classification of arrhythmia and can be helpful for assisting clinicians by enabling real-time detection of cardiac arrhythmias during routine ECG screenings.},
	language = {en},
	number = {1},
	urldate = {2025-09-08},
	journal = {Scientific Reports},
	author = {Bai, Xiangyun and Dong, Xinglong and Li, Yabing and Liu, Ruixia and Zhang, Henggui},
	month = oct,
	year = {2024},
	note = {Publisher: Nature Publishing Group},
	keywords = {Biomedical engineering, Computational biology and bioinformatics, Computational science, Computer science, Physiology},
	pages = {24441},
}

@misc{noauthor_point--care_nodate,
	title = {Point-of-{Care} {Ultrasound} {Assessment} of the {Carotid} {Artery} during {Cardiopulmonary} {Resuscitation}},
	url = {https://www.acep.org/criticalcare/newsroom/newsroom-articles/september2022/point-of-care-ultrasound-assessment-of-the-carotid-artery-during-cardiopulmonary-resuscitation},
	abstract = {Rising Stars Article by: Samuel G. Rouleau, MD, Roderick Fontenette, MD, MHCM, CPE, FACEP, FCCM, and Nicholas J. Johnson, MD, FACEP, FCCM},
	language = {en},
	urldate = {2025-07-10},
}

@article{kruger_realtime_2024,
	title = {Realtime {Detection} of {Spontaneous} {Circulation} in {Humans} {During} {Cardiopulmonary} {Resuscitation} {Using} a {Continuous} {Hands}-{Free} {Carotid} {Doppler}: {A} {Cardiac} {Arrest} {Pilot} {StudyRealtime} {Detection} of {Spontaneous} {Circulation} in {Humans} {During} {Cardiopulmonary} {Resuscitation} {Using} a {Continuous} {Hands}-{Free} {Carotid} {Doppler}: {A} {Cardiac} {Arrest} {Pilot} {Study}},
	volume = {150},
	url = {https://www.ahajournals.org/doi/10.1161/CIR.0000000000001299},
	doi = {10.1161/CIR.0000000000001299},
	number = {25},
	urldate = {2025-06-02},
	journal = {Circulation},
	author = {Kruger, Guro and Hansen, Oystein and Faldaas, Bjoern and Birkeli, Sunniva and Flage, John Helge and Hautois, Gregory and Norvik, Anders and Lund, Hedda and Kiss, Gabriel and Torp, Hans and Hergum, Torbjorn and Urteaga, Jon and Skogvoll, Eirik and Ingul, Charlotte},
	month = dec,
	year = {2024},
	note = {Publisher: American Heart Association},
	pages = {e712--e763},
}

@article{cohen_femoral_2022,
	title = {Femoral artery {Doppler} ultrasound is more accurate than manual palpation for pulse detection in cardiac arrest},
	volume = {173},
	issn = {0300-9572},
	url = {https://www.sciencedirect.com/science/article/pii/S0300957222000326},
	doi = {10.1016/j.resuscitation.2022.01.030},
	abstract = {Objectives
Our primary objective was to assess the accuracy of Doppler ultrasound versus manual palpation in detecting any pulse with an arterial line waveform in cardiac arrest. Secondarily, we sought to determine whether peak systolic velocity (PSV) on Doppler ultrasound could detect a pulse with a systolic blood pressure (SBP) ≥ 60 mmHg.
Methods
We conducted a prospective, cross-sectional, diagnostic accuracy study on a convenience sample of adult, Emergency Department (ED) cardiac arrest patients. All patients had a femoral arterial line. During a pulse check, manual pulse detection, PSV and Doppler ultrasound clips, and SBP were recorded. A receiver operator characteristic curve analysis was performed to determine the optimal cut-off of PSV associated with a SBP ≥ 60 mmHg. Accuracy of manual palpation and Doppler ultrasound for detection of any pulse and SBP ≥ 60 mmHg were compared with McNemar’s test.
Results
54 patients and 213 pulse checks were analysed. Doppler ultrasound demonstrated higher accuracy than manual palpation (95.3\% vs. 54.0\%; p {\textless} 0.001) for detection of any pulse. Correlation between PSV and SBP was strong (Spearman correlation coefficient = 0.89; p {\textless} 0.001). The optimal cut-off value of PSV associated with a SBP ≥ 60 mmHg was 20 cm/s (area under the curve = 0.975). To detect SBP ≥ 60 mmHg, accuracy of a PSV ≥ 20 cm/s was higher than manual palpation (91.4\% vs. 66.2\%; p {\textless} 0.001).
Conclusions
Among ED cardiac arrest patients, femoral artery Doppler ultrasound was more accurate than manual palpation for detecting any pulse. When using a PSV ≥ 20 cm/s, Doppler ultrasound was also more accurate for detecting a SBP ≥ 60 mmHg.},
	urldate = {2025-05-31},
	journal = {Resuscitation},
	author = {Cohen, Allison L. and Li, Timmy and Becker, Lance B. and Owens, Casey and Singh, Neha and Gold, Allen and Nelson, Mathew J. and Jafari, Daniel and Haddad, Ghania and Nello, Alexander V. and Rolston, Daniel M. and Sison, Cristina and Lesser, Martin L.},
	month = apr,
	year = {2022},
	keywords = {Cardiac arrest, Cardiopulmonary resuscitation, Doppler ultrasound, Pulse detection},
	pages = {156--165},
}

@article{haddad_doppler_2023,
	title = {Doppler ultrasound peak systolic velocity versus end tidal carbon dioxide during pulse checks in cardiac arrest},
	volume = {183},
	issn = {0300-9572},
	url = {https://www.sciencedirect.com/science/article/pii/S0300957223000084},
	doi = {10.1016/j.resuscitation.2023.109695},
	abstract = {Background
An accurate, non-invasive measure of return of spontaneous circulation (ROSC) is needed to improve management of cardiac arrest patients.
Objectives
During a pulse check in Emergency Department (ED) cardiac arrest patients, we compared the correlation between 1) end tidal carbon dioxide (ETCO2) and systolic blood pressure (SBP), and 2) Doppler ultrasound peak systolic velocity (PSV) and SBP. Additionally, we assessed the accuracy of PSV ≥ 20 cm/sec in comparison to previously suggested ETCO2 ≥ 20 or ≥ 25 mmHg thresholds to predict ROSC with SBP ≥ 60 mmHg.
Methods
This was a secondary analysis of a previously published prospective observational study of ED cardiac arrest patients with an advanced airway and femoral arterial line in place. During each pulse check, highest SBP, highest PSV, and ETCO2 at the end of the pulse check were recorded. Spearman correlation coefficients were calculated and compared using a Fisher Z-transformation. Accuracy of previously determined PSV and ETCO2 thresholds for detecting ROSC with SBP ≥ 60 mmHg were compared using McNemar’s tests.
Results
Based on data from 35 patients with 111 pulse checks, we found a higher correlation between PSV and SBP than ETCO2 and SBP (0.71 vs 0.31; p {\textless} 0.001). Diagnostic accuracy of PSV ≥ 20 cm/sec for detecting ROSC with SBP ≥ 60 mmHg was 89\% (95\% CI: 82\%-94\%) versus 59\% (95\% CI: 49\%-68\%) and 58\% (95\% CI: 48\%-67\%) for ETCO2 ≥ 20 and ≥ 25 mmHg, respectively.
Conclusions
During a pulse check, Doppler ultrasound PSV outperformed ETCO2 for correlation with SBP and accuracy in detecting ROSC with SBP ≥ 60 mmHg.},
	urldate = {2025-05-30},
	journal = {Resuscitation},
	author = {Haddad, Ghania and Margius, Deanna and Cohen, Allison L. and Gorlin, Margaret and Jafari, Daniel and Li, Timmy and Owens, Casey and Becker, Lance and Rolston, Daniel M.},
	month = feb,
	year = {2023},
	keywords = {ACLS, CPR, Doppler, ETCO2, PSV, ROSC, Ultrasound},
	pages = {109695},
}

@article{zylyftari_contacts_2020,
	title = {Contacts to the healthcare system prior to out-of-hospital cardiac arrests},
	volume = {41},
	issn = {0195-668X},
	url = {https://doi.org/10.1093/ehjci/ehaa946.0766},
	doi = {10.1093/ehjci/ehaa946.0766},
	abstract = {Patients who suffer a sudden out-of-hospital cardiac arrest (OHCA) may be preceded by warning symptoms and healthcare system contact. Though, is currently difficult early identification of sudden cardiac arrest patients.We aimed to examine contacts with the healthcare system up to two weeks and one year before OHCA.OHCA patients were identified from the Danish Cardiac Arrest Registry (2001–2014). The pattern of healthcare contacts (with either general practitioner (GP) or hospital) within the year prior to OHCA of OHCA patients was compared with that of 9 sex- and age-matched controls from the background general population. Additionally, we evaluated characteristics of OHCA patients according to the type of healthcare contact (GP/hospital/both/no-contact) and the including characteristics of contacts, within two weeks prior their OHCA event.Out of 28,955 OHCA patients (median age of 72 (62–81) years and with 67\% male) of presumed cardiac cause, 16,735 (57.8\%) contacted the healthcare system (GP and hospital) within two weeks prior to OHCA. From one year before OHCA, the weekly percentages of contacts to GP were relatively constant (26\%) until within 2 weeks prior to OHCA where they markedly increased (54\%). In comparison, 14\% of the general population contacted the GP during the same period (Figure). The weekly percentages of contacts with hospitals gradually increased in OHCA patients from 3.5\% to 6.5\% within 6 months, peaking at the second week (6.8\%), prior to OHCA. In comparison, only 2\% of the general population had a hospital contact in that period (Figure). Within 2 weeks of OHCA, patients contacted GP mainly by telephone (71.6\%). Hospital diagnoses were heterogenous, where ischemic heart disease (8\%) and heart failure (4.5\%) were the most frequent.There is an increase in healthcare contacts prior to “sudden” OHCA and overall, 54\% of OHCA-patients had contacted GP within 2 weeks before the event. This could have implications for developing future strategies for early identification of patients prior to their cardiac arrest.Figure 1. The weekly percentages of contacts to GP (red) and hospital (blue) within one year before OHCA comparing the OHCA cases to the age- and sex-matched control population (N cases = 28,955; N controls = 260,595).Type of funding source: Public grant(s) – EU funding. Main funding source(s): European Union's Horizon 2020},
	number = {Supplement\_2},
	urldate = {2025-05-30},
	journal = {European Heart Journal},
	author = {Zylyftari, N and Moller, S.G and Wissenberg, M and Folke, F and Barcella, C.A and Moller, A.L and Mills, E.H.A and Tan, H.L and Kober, L and Lippert, F and Gislason, G.H and Pedersen, C.T},
	month = nov,
	year = {2020},
	pages = {ehaa946.0766},
}

@misc{noauthor_european_nodate,
	title = {European {Heart} {Journal} {\textbar} {Oxford} {Academic}},
	url = {https://academic.oup.com/eurheartj/eurheartj},
	abstract = {An official international, peer reviewed journal of the European Society of Cardiology (ESC). The Journal publishes the highest quality material, both clinical and scientific, on all aspects of cardiovascular medicine. The Journal also publishes the ESC Guidelines.},
	language = {en},
	urldate = {2025-05-30},
	journal = {OUP Academic},
}

@article{sobotka_effects_2015,
	title = {Effects of chest compressions on cardiovascular and cerebral hemodynamics in asphyxiated near-term lambs},
	volume = {78},
	copyright = {2015 International Pediatric Research Foundation, Inc.},
	issn = {1530-0447},
	url = {https://www.nature.com/articles/pr2015117},
	doi = {10.1038/pr.2015.117},
	abstract = {Chest compressions (CC) and adrenaline administration are recommended in asphyxiated newborns with persistent bradycardia despite effective ventilation. The effects of CC on cerebral blood flow in newborns at birth are unknown. Our aim was to determine the effects of CC, with or without adrenaline administration, on the return of spontaneous circulation, carotid blood flow (CBF), and carotid arterial pressure (CAP) in asphyxiated near-term lambs.},
	language = {en},
	number = {4},
	urldate = {2025-03-24},
	journal = {Pediatric Research},
	author = {Sobotka, Kristina S. and Polglase, Graeme R. and Schmölzer, Georg M. and Davis, Peter G. and Klingenberg, Claus and Hooper, Stuart B.},
	month = oct,
	year = {2015},
	note = {Publisher: Nature Publishing Group},
	keywords = {Paediatric research},
	pages = {395--400},
}

@article{faldaas_real-time_2024,
	title = {Real-time feedback on chest compression efficacy by hands-free carotid {Doppler} in a porcine model},
	volume = {18},
	issn = {2666-5204},
	url = {https://www.sciencedirect.com/science/article/pii/S2666520424000341},
	doi = {10.1016/j.resplu.2024.100583},
	abstract = {Aim
Current guidelines for cardiopulmonary resuscitation (CPR) recommend a one-size-fits-all approach in relation to the positioning of chest compressions. We recently developed RescueDoppler, a hands-free Doppler ultrasound device for continuous monitoring of carotid blood flow velocity during CPR. The aim of the present study is to investigate whether RescueDoppler via real-time hemodynamic feedback, could identify both optimal and suboptimal compression positions.
Methods
In this model of animal cardiac arrest, we induced ventricular fibrillation in five domestic pigs. Manual chest compressions were performed for ten seconds at three different positions on the sternum in random order and repeated six times. We analysed Time Average Velocity (TAV) with chest compression position as a fixed effect and animal, position, and sequential time within animals as random effects. Furthermore, we compared TAV to invasive blood pressure from the contralateral carotid artery.
Results
We were able to detect changes in TAV when altering positions. The positions with the highest (range 19 to 48 cm/s) and lowest (6–25 cm/s) TAV were identified in all animals, with corresponding peak pressure 50–81 mmHg, and 46–64 mmHg, respectively. Blood flow velocity was, on average, highest at the middle position (TAV 33 cm/s), but with significant variability between animals (SD 2.8) and positions within the same animal (SD 9.3).
Conclusion
RescueDoppler detected TAV changes during CPR with alternating chest compression positions, identifying the position yielding maximal TAV. Future clinical studies should investigate if RescueDoppler can be used as a real-time hemodynamical feedback device to guide compression position.},
	urldate = {2025-03-24},
	journal = {Resuscitation Plus},
	author = {Faldaas, Bjørn Ove and Nielsen, Erik Waage and Storm, Benjamin Stage and Lappegård, Knut Tore and Nilsen, Bent Aksel and Kiss, Gabriel and Skogvoll, Eirik and Torp, Hans and Ingul, Charlotte Björk},
	month = jun,
	year = {2024},
	keywords = {Cardiopulmonary resuscitation (CPR), Carotid artery, Doppler ultrasound, Manual chest compressions, Physiologic monitoring, Real-time feedback},
	pages = {100583},
}

@article{faldaas_hands-free_2024,
	title = {A hands-free carotid {Doppler} can identify spontaneous circulation without interrupting cardiopulmonary resuscitation: an animal study},
	volume = {12},
	issn = {2197-425X},
	shorttitle = {A hands-free carotid {Doppler} can identify spontaneous circulation without interrupting cardiopulmonary resuscitation},
	url = {https://doi.org/10.1186/s40635-024-00704-w},
	doi = {10.1186/s40635-024-00704-w},
	abstract = {Identifying spontaneous circulation during cardiopulmonary resuscitation (CPR) is challenging. Current methods, which involve intermittent and time-consuming pulse checks, necessitate pauses in chest compressions. This issue is problematic in both in-hospital cardiac arrest and out-of-hospital cardiac arrest situations, where resources for identifying circulation during CPR may be limited. The fraction of chest compression plays a pivotal role in improving survival rates. To address this challenge, we evaluated a newly developed hands-free, continuous carotid Doppler system (RescueDoppler), designed to identify spontaneous circulation during chest compressions. In our study, we utilized a porcine model of cardiac arrest to investigate sequences of ventricular fibrillation, followed by defibrillation, and monitoring for the return of spontaneous circulation during chest compressions with the carotid Doppler system. We explored both manual compressions at 100 and 50 compressions per minute and mechanical compressions. To estimate the detection rate (i.e., sensitivity), we employed a logistic mixed model with animal identity as random effect.},
	number = {1},
	urldate = {2025-03-24},
	journal = {Intensive Care Medicine Experimental},
	author = {Faldaas, Bjørn Ove and Storm, Benjamin Stage and Lappegård, Knut Tore and How, Ole-Jakob and Nilsen, Bent Aksel and Kiss, Gabriel and Skogvoll, Eirik and Nielsen, Erik Waage and Torp, Hans and Ingul, Charlotte Björk},
	month = dec,
	year = {2024},
	keywords = {Cardiac arrest, Cardiac arrest monitor, Cardiopulmonary resuscitation, Carotid artery blood flow, Chest compression, Doppler, Feedback device, Return of spontaneous circulation, Ultrasound},
	pages = {121},
}

@article{youness_review_2016,
	title = {Review and {Outcome} of {Prolonged} {Cardiopulmonary} {Resuscitation}},
	volume = {2016},
	issn = {2090-1305},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4738728/},
	doi = {10.1155/2016/7384649},
	abstract = {The maximal duration of cardiopulmonary resuscitation (CPR) is unknown. We report a case of prolonged CPR. We have then reviewed all published cases with CPR duration equal to or more than 20 minutes. The objective was to determine the survival rate, the neurological outcome, and the characteristics of the survivors. Measurements and Main Results. The CPR data for 82 patients was reviewed. The median duration of CPR was 75 minutes. Patients mean age was 43 ± 21 years with no significant comorbidities. The main causes of the cardiac arrests were myocardial infarction (29\%), hypothermia (21\%), and pulmonary emboli (12\%). 74\% of the arrests were witnessed, with a mean latency to CPR of 2 ± 6 minutes and good quality chest compression provided in 96\% of the cases. Adjunct therapy included extracorporeal membrane oxygenation (18\%), thrombolysis (15.8\%), and rewarming for hypothermia (19.5\%). 83\% were alive at 1 year, with full neurological recovery reported in 63 patients. Conclusion. Patients undergoing prolonged CPR can survive with good outcome. Young age, myocardial infarction, and potentially reversible causes of cardiac arrest such as hypothermia and pulmonary emboli predict a favorable result, especially when the arrest is witnessed and followed by prompt and good resuscitative efforts.},
	urldate = {2025-03-24},
	journal = {Critical Care Research and Practice},
	author = {Youness, Houssein and Al Halabi, Tarek and Hussein, Hussein and Awab, Ahmed and Jones, Kellie and Keddissi, Jean},
	year = {2016},
	pmid = {26885387},
	pmcid = {PMC4738728},
	pages = {7384649},
}

@article{reynolds_duration_2013,
	title = {Duration of {Resuscitation} {Efforts} and {Functional} {Outcome} {After} {Out}-of-{Hospital} {Cardiac} {Arrest}},
	volume = {128},
	url = {https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.113.002408},
	doi = {10.1161/CIRCULATIONAHA.113.002408},
	abstract = {Background—Functionally favorable survival remains low after out-of-hospital cardiac arrest. When initial interventions fail to achieve the return of spontaneous circulation, they are repeated with little incremental benefit. Patients without rapid return of spontaneous circulation do not typically survive with good functional outcome. Novel approaches to out-of-hospital cardiac arrest have yielded functionally favorable survival in patients for whom traditional measures had failed, but the optimal transition point from traditional measures to novel therapies is ill defined. Our objective was to estimate the dynamic probability of survival and functional recovery as a function of resuscitation effort duration to identify this transition point.Methods and Results—Retrospective cohort study of a cardiac arrest database at a single site. We included 1014 adult (≥18 years) patients experiencing nontraumatic out-of-hospital cardiac arrest between 2005 and 2011, defined as receiving cardiopulmonary resuscitation or defibrillation from a professional provider. We stratified by functional outcome at hospital discharge (modified Rankin scale). Survival to hospital discharge was 11\%, but only 6\% had a modified Rankin scale of 0 to 3. Within 16.1 minutes of cardiopulmonary resuscitation, 89.7\% (95\% confidence interval, 80.3\%–95.8\%) of patients with good functional outcome had achieved return of spontaneous circulation, and the probability of good functional recovery fell to 1\%. Adjusting for prehospital and inpatient covariates, cardiopulmonary resuscitation duration (minutes) is independently associated with favorable functional status at hospital discharge (odds ratio, 0.84; 95\% confidence interval, 0.72–0.98; P=0.02).Conclusions—The probability of survival to hospital discharge with a modified Rankin scale of 0 to 3 declines rapidly with each minute of cardiopulmonary resuscitation. Novel strategies should be tested early after cardiac arrest rather than after the complete failure of traditional measures.},
	number = {23},
	urldate = {2025-03-24},
	journal = {Circulation},
	author = {Reynolds, Joshua C. and Frisch, Adam and Rittenberger, Jon C. and Callaway, Clifton W.},
	month = dec,
	year = {2013},
	note = {Publisher: American Heart Association},
	pages = {2488--2494},
}

@article{balakrishnan_cause_2023,
	title = {The {Cause} and {Effect} of {Prolonged} {Cardiac} {Arrest}},
	volume = {163},
	issn = {0012-3692},
	url = {https://journal.chestnet.org/article/S0012-3692(22)04213-1/abstract},
	doi = {10.1016/j.chest.2022.06.050},
	abstract = {A 41-year-old woman with a history of antiphospholipid antibody syndrome came to the
ED after experiencing a witnessed cardiac arrest with return of spontaneous circulation
after 20 min of CPR. She had a second cardiac arrest on arrival at the ED with return
of spontaneous circulation after 15 min of CPR. The patient was intubated but remained
in shock despite vasopressor support. Critical care was consulted for assistance with
postcardiac arrest care. Initial bloodwork was pertinent for a hemoglobin level of
9.0 g/dL and lactic acid level of 19.0 mM.},
	language = {English},
	number = {3},
	urldate = {2025-03-24},
	journal = {CHEST},
	author = {Balakrishnan, Rithvik and Shiloh, Ariel L.},
	month = mar,
	year = {2023},
	pmid = {36894267},
	note = {Publisher: Elsevier},
	pages = {e125--e127},
}

@article{lauridsen_association_2024,
	title = {Association {Between} {Chest} {Compression} {Pause} {Duration} and {Survival} {After} {Pediatric} {In}-{Hospital} {Cardiac} {Arrest}},
	volume = {149},
	url = {https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.123.066882},
	doi = {10.1161/CIRCULATIONAHA.123.066882},
	abstract = {BACKGROUND:The association between chest compression (CC) pause duration and pediatric in-hospital cardiac arrest survival outcomes is unknown. The American Heart Association has recommended minimizing pauses in CC in children to {\textless}10 seconds, without supportive evidence. We hypothesized that longer maximum CC pause durations are associated with worse survival and neurological outcomes.METHODS:In this cohort study of index pediatric in-hospital cardiac arrests reported in pediRES-Q (Quality of Pediatric Resuscitation in a Multicenter Collaborative) from July of 2015 through December of 2021, we analyzed the association in 5-second increments of the longest CC pause duration for each event with survival and favorable neurological outcome (Pediatric Cerebral Performance Category ≤3 or no change from baseline). Secondary exposures included having any pause {\textgreater}10 seconds or {\textgreater}20 seconds and number of pauses {\textgreater}10 seconds and {\textgreater}20 seconds per 2 minutes.RESULTS:We identified 562 index in-hospital cardiac arrests (median [Q1, Q3] age 2.9 years [0.6, 10.0], 43\% female, 13\% shockable rhythm). Median length of the longest CC pause for each event was 29.8 seconds (11.5, 63.1). After adjustment for confounders, each 5-second increment in the longest CC pause duration was associated with a 3\% lower relative risk of survival with favorable neurological outcome (adjusted risk ratio, 0.97 [95\% CI, 0.95–0.99]; P=0.02). Longest CC pause duration was also associated with survival to hospital discharge (adjusted risk ratio, 0.98 [95\% CI, 0.96–0.99]; P=0.01) and return of spontaneous circulation (adjusted risk ratio, 0.93 [95\% CI, 0.91–0.94]; P{\textless}0.001). Secondary outcomes of any pause {\textgreater}10 seconds or {\textgreater}20 seconds and number of CC pauses {\textgreater}10 seconds and {\textgreater}20 seconds were each significantly associated with adjusted risk ratio of return of spontaneous circulation, but not survival or neurological outcomes.CONCLUSIONS:Each 5-second increment in longest CC pause duration during pediatric in-hospital cardiac arrest was associated with lower chance of survival with favorable neurological outcome, survival to hospital discharge, and return of spontaneous circulation. Any CC pause {\textgreater}10 seconds or {\textgreater}20 seconds and number of pauses {\textgreater}10 seconds and {\textgreater}20 seconds were significantly associated with lower adjusted probability of return of spontaneous circulation, but not survival or neurological outcomes.},
	number = {19},
	urldate = {2025-03-24},
	journal = {Circulation},
	author = {Lauridsen, Kasper G. and Morgan, Ryan W. and Berg, Robert A. and Niles, Dana E. and Kleinman, Monica E. and Zhang, Xuemei and Griffis, Heather and Del Castillo, Jimena and Skellett, Sophie and Lasa, Javier J. and Raymond, Tia T. and Sutton, Robert M. and Nadkarni, Vinay M. and {for the pediRES-Q Investigators}},
	month = may,
	year = {2024},
	note = {Publisher: American Heart Association},
	pages = {1493--1500},
}

@article{cherry_modeling_2015,
	title = {Modeling cardiac arrest and resuscitation in the domestic pig},
	volume = {4},
	url = {https://www.wjgnet.com/2220-3141/full/v4/i1/1.htm},
	doi = {10.5492/wjccm.v4.i1.1},
	abstract = {Modeling cardiac arrest and resuscitation in the domestic pig},
	language = {en},
	number = {1},
	urldate = {2025-03-19},
	journal = {World Journal of Critical Care Medicine},
	author = {Cherry, Brandon H. and Nguyen, Anh Q. and Hollrah, Roger A. and Olivencia-Yurvati, Albert H. and Mallet, Robert T.},
	month = feb,
	year = {2015},
	note = {Publisher: Baishideng Publishing Group Inc.},
	pages = {1--12},
}

@incollection{liu_prediction_2020,
	title = {Prediction of {ROSC} {After} {Cardiac} {Arrest} {Using} {Machine} {Learning}},
	url = {https://ebooks.iospress.nl/doi/10.3233/SHTI200440},
	urldate = {2025-03-17},
	booktitle = {Digital {Personalized} {Health} and {Medicine}},
	publisher = {IOS Press},
	author = {Liu, Nan and Ho, Andrew Fu Wah and Pek, Pin Pin and Lu, Tsung-Chien and Khruekarnchana, Pairoj and Song, Kyoung Jun and Tanaka, Hideharu and Naroo, Ghulam Yasin and Gan, Han Nee and Koh, Zhi Xiong and Ma, Huei-Ming and Ong, Marcus},
	year = {2020},
	doi = {10.3233/SHTI200440},
	pages = {1357--1358},
}

@article{shin_evaluation_2023,
	title = {Evaluation of optimal scene time interval for out-of-hospital cardiac arrest using a deep neural network},
	volume = {63},
	issn = {0735-6757},
	url = {https://www.sciencedirect.com/science/article/pii/S073567572200643X},
	doi = {10.1016/j.ajem.2022.10.011},
	abstract = {Aim
This study aims to develop a cardiac arrest prediction model using deep learning (CAPD) algorithm and to validate the developed algorithm by evaluating the change in out-of-hospital cardiac arrest patient prognosis according to the increase in scene time interval (STI).
Methods
We conducted a retrospective cohort study using smart advanced life support trial data collected by the National Emergency Center from January 2016 to December 2019. The smart advanced life support data were randomly partitioned into derivation and validation datasets. The performance of the CAPD model using the patient's age, sex, event witness, bystander cardiopulmonary resuscitation (CPR), administration of epinephrine, initial shockable rhythm, prehospital defibrillation, provision of advanced life support, response time interval, and STI as prediction variables for prediction of a patient's prognosis was compared with conventional machine learning methods. After fixing other values of the input data, the changes in prognosis of the patient with respect to the increase in STI was observed.
Results
A total of 16,992 patients were included in this study. The area under the receiver operating characteristic curve values for predicting prehospital return of spontaneous circulation (ROSC) and favorable neurological outcomes were 0.828 (95\% confidence interval 0.826–0.830) and 0.907 (0.914–0.910), respectively. Our algorithm significantly outperformed other artificial intelligence algorithms and conventional methods. The neurological recovery rate was predicted to decrease to 1/3 of that at the beginning of cardiopulmonary resuscitation when the STI was 28 min, and the prehospital ROSC was predicted to decrease to 1/2 of its initial level when the STI was 30 min.
Conclusion
The CAPD exhibits potential and effectiveness in identifying patients with ROSC and favorable neurological outcomes for prehospital resuscitation.},
	urldate = {2025-03-17},
	journal = {The American Journal of Emergency Medicine},
	author = {Shin, Seung Jae and Bae, Hee Sun and Moon, Hyung Jun and Kim, Gi Woon and Cho, Young Soon and Lee, Dong Wook and Jeong, Dong Kil and Kim, Hyun Joon and Lee, Hyun Jung},
	month = jan,
	year = {2023},
	keywords = {Cardiopulmonary resuscitation, Deep learning, Emergency medical service, Out-of-hospital cardiac arrest, Return of spontaneous circulation, Scene time interval},
	pages = {29--37},
}

@article{krizmaric_intelligent_2009,
	title = {Intelligent analysis in predicting outcome of out-of-hospital cardiac arrest},
	volume = {95},
	issn = {0169-2607},
	url = {https://www.sciencedirect.com/science/article/pii/S0169260709000819},
	doi = {10.1016/j.cmpb.2009.02.013},
	abstract = {The prognosis among patients who suffer out-of-hospital cardiac arrest is poor. Higher survival rates have been observed only in patients with ventricular fibrillation who were fortunate enough to have basic and advanced life support initiated early after cardiac arrest. The ability to predict outcomes of cardiac arrest would be useful for resuscitation chains. Levels of EtCO2in expired air from lungs during cardiopulmonary resuscitation may serve as a non-invasive predictor of successful resuscitation and survival from cardiac arrest. Six different supervised learning classification techniques were used and evaluated. It has been shown that machine learning methods can provide an efficient way to detect important prognostic factors upon which further emergency unit actions are based.},
	number = {2, Supplement},
	urldate = {2025-03-17},
	journal = {Computer Methods and Programs in Biomedicine},
	author = {Krizmaric, Miljenko and Verlic, Mateja and Stiglic, Gregor and Grmec, Stefek and Kokol, Peter},
	month = aug,
	year = {2009},
	keywords = {Cardiac arrest, Machine learning, Out-of-hospital resuscitation},
	pages = {S22--S32},
}

@article{noauthor_pdf_2024,
	title = {({PDF}) {Artificial} intelligence to support out-of-hospital cardiac arrest care: {A} scoping review},
	shorttitle = {({PDF}) {Artificial} intelligence to support out-of-hospital cardiac arrest care},
	url = {https://www.researchgate.net/publication/375662363_Artificial_intelligence_to_support_out-of-hospital_cardiac_arrest_care_A_scoping_review},
	doi = {10.1016/j.resplu.2023.100491},
	abstract = {PDF {\textbar} Background Artificial intelligence (AI) has demonstrated significant potential in supporting emergency medical services personnel during... {\textbar} Find, read and cite all the research you need on ResearchGate},
	language = {en},
	urldate = {2025-03-17},
	journal = {ResearchGate},
	month = dec,
	year = {2024},
}

@article{nanayakkara_characterising_2018,
	title = {Characterising risk of in-hospital mortality following cardiac arrest using machine learning: {A} retrospective international registry study},
	volume = {15},
	issn = {1549-1676},
	shorttitle = {Characterising risk of in-hospital mortality following cardiac arrest using machine learning},
	url = {https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002709},
	doi = {10.1371/journal.pmed.1002709},
	abstract = {Background Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admission, we aimed to develop more accurate models of risk prediction using both logistic regression (LR) and machine learning (ML) techniques, with a combination of demographic, physiologic, and biochemical information. Methods and findings Patient-level data were extracted from the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database for patients who had experienced a cardiac arrest within 24 hours prior to admission to an intensive care unit (ICU) during the period January 2006 to December 2016. The primary outcome was in-hospital mortality. The models were trained and tested on a dataset (split 90:10) including age, lowest and highest physiologic variables during the first 24 hours, and key past medical history. LR and 5 ML approaches (gradient boosting machine [GBM], support vector classifier [SVC], random forest [RF], artificial neural network [ANN], and an ensemble) were compared to the APACHE III and Australian and New Zealand Risk of Death (ANZROD) predictions. In all, 39,566 patients from 186 ICUs were analysed. Mean (±SD) age was 61 ± 17 years; 65\% were male. Overall in-hospital mortality was 45.5\%. Models were evaluated in the test set. The APACHE III and ANZROD scores demonstrated good discrimination (area under the receiver operating characteristic curve [AUROC] = 0.80 [95\% CI 0.79–0.82] and 0.81 [95\% CI 0.8–0.82], respectively) and modest calibration (Brier score 0.19 for both), which was slightly improved by LR (AUROC = 0.82 [95\% CI 0.81–0.83], DeLong test, p {\textless} 0.001). Discrimination was significantly improved using ML models (ensemble and GBM AUROCs = 0.87 [95\% CI 0.86–0.88], DeLong test, p {\textless} 0.001), with an improvement in performance (Brier score reduction of 22\%). Explainability models were created to assist in identifying the physiologic features that most contributed to an individual patient’s survival. Key limitations include the absence of pre-hospital data and absence of external validation. Conclusions ML approaches significantly enhance predictive discrimination for mortality following cardiac arrest compared to existing illness severity scores and LR, without the use of pre-hospital data. The discriminative ability of these ML models requires validation in external cohorts to establish generalisability.},
	language = {en},
	number = {11},
	urldate = {2025-03-17},
	journal = {PLOS Medicine},
	author = {Nanayakkara, Shane and Fogarty, Sam and Tremeer, Michael and Ross, Kelvin and Richards, Brent and Bergmeir, Christoph and Xu, Sheng and Stub, Dion and Smith, Karen and Tacey, Mark and Liew, Danny and Pilcher, David and Kaye, David M.},
	month = nov,
	year = {2018},
	note = {Publisher: Public Library of Science},
	keywords = {Artificial neural networks, Cardiac arrest, Forecasting, Intensive care units, Machine learning, Machine learning algorithms, Medical risk factors, Support vector machines},
	pages = {e1002709},
}

@article{okada_ai_2023,
	title = {{AI} and machine learning in resuscitation: {Ongoing} research, new concepts, and key challenges},
	volume = {15},
	issn = {2666-5204},
	shorttitle = {{AI} and machine learning in resuscitation},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10400904/},
	doi = {10.1016/j.resplu.2023.100435},
	abstract = {•
              Various types of research in AI and machine learning, specifically in the fields of resuscitation, emergency medicine, and critical care medicine, include.
            
            
              •
              Various AI and machine learning play distinct roles and have the potential to provide valuable insights for optimizing clinical workflows.
            
            
              •
              However, the implementation of AI and machine learning in clinical settings presents challenges.
            
            
              •
              Seamless integration into clinical practice is crucial for practical implementation.
            
            
              •
              Self-fulfilling prophecies and feedback loops pose additional challenges, as they mask false positives and can reinforce and amplify biases in clinical situtations.
            
            
              •
              Ensuring the development trustworthy AI and ML models requires considerations of transparency, and interpretability, of the models.
            
            
              •
              Effective collaboration with experts is indispensable to harness the potential of AI and ML in the fields of resuscitation.},
	urldate = {2025-03-17},
	journal = {Resuscitation Plus},
	author = {Okada, Yohei and Mertens, Mayli and Liu, Nan and Lam, Sean Shao Wei and Ong, Marcus Eng Hock},
	month = jul,
	year = {2023},
	pmid = {37547540},
	pmcid = {PMC10400904},
	pages = {100435},
}

@article{viderman_artificial_2023,
	title = {Artificial {Intelligence} in {Resuscitation}: {A} {Scoping} {Review}},
	volume = {12},
	issn = {2077-0383},
	shorttitle = {Artificial {Intelligence} in {Resuscitation}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10054374/},
	doi = {10.3390/jcm12062254},
	abstract = {Introduction: Cardiac arrest is a significant cause of premature mortality and severe disability. Despite the death rate steadily decreasing over the previous decade, only 22\% of survivors achieve good clinical status and only 25\% of patients survive until their discharge from the hospital. The objective of this scoping review was to review relevant AI modalities and the main potential applications of AI in resuscitation. Methods: We conducted the literature search for related studies in PubMed, EMBASE, and Google Scholar. We included peer-reviewed publications and articles in the press, pooling and characterizing the data by their model types, goals, and benefits. Results: After identifying 268 original studies, we chose 59 original studies (reporting 1,817,419 patients) to include in the qualitative synthesis. AI-based methods appear to be superior to traditional methods in achieving high-level performance. Conclusion: AI might be useful in predicting cardiac arrest, heart rhythm disorders, and post-cardiac arrest outcomes, as well as in the delivery of drone-delivered defibrillators and notification of dispatchers. AI-powered technologies could be valuable assistants to continuously track patient conditions. Healthcare professionals should assist in the research and development of AI-powered technologies as well as their implementation into clinical practice.},
	number = {6},
	urldate = {2025-03-17},
	journal = {Journal of Clinical Medicine},
	author = {Viderman, Dmitriy and Abdildin, Yerkin G. and Batkuldinova, Kamila and Badenes, Rafael and Bilotta, Federico},
	month = mar,
	year = {2023},
	pmid = {36983255},
	pmcid = {PMC10054374},
	pages = {2254},
}

@incollection{demers_physiology_2025,
	address = {Treasure Island (FL)},
	title = {Physiology, {Mean} {Arterial} {Pressure}},
	copyright = {Copyright © 2025, StatPearls Publishing LLC.},
	url = {http://www.ncbi.nlm.nih.gov/books/NBK538226/},
	abstract = {The definition of mean arterial pressure (MAP) is the average arterial pressure throughout one cardiac cycle, systole, and diastole. MAP is influenced by cardiac output and systemic vascular resistance, each of which is influenced by several variables. These will be discussed further under the Mechanism heading of this article. Cardiac output is calculated as the product of heart rate and stroke volume. The determination of stroke volume is by ventricular inotropy and preload. Preload is affected by blood volume and the compliance of veins. Increasing the blood volume increases the preload, increasing the stroke volume and therefore increasing cardiac output. Afterload also affects the stroke volume in that an increase in afterload will decrease stroke volume. Heart rate is affected by the chronotropy, dromotropy, and lusitropy of the myocardium. Systemic vascular resistance is determined primarily by the radius of the blood vessels. Decreasing the radius of the vessels increases vascular resistance. Increasing the radius of the vessels would have the opposite effect. Blood viscosity can also affect systemic vascular resistance. An increase in hematocrit will increase blood viscosity and increase systemic vascular resistance. Viscosity, however, is considered only to play a minor role in systemic vascular resistance. A common method used to estimate the MAP is the following formula: MAP = DP + 1/3(SP – DP) or MAP = DP + 1/3(PP). Where DP is the diastolic blood pressure, SP is the systolic blood pressure, and PP is the pulse pressure. This method is often more conducive to measuring MAP in most clinical settings as it offers a quick means of calculation if the blood pressure is known.},
	language = {eng},
	urldate = {2025-03-17},
	booktitle = {{StatPearls}},
	publisher = {StatPearls Publishing},
	author = {DeMers, Daniel and Wachs, Daliah},
	year = {2025},
	pmid = {30855814},
}

@article{pothiawala_post-resuscitation_2017,
	title = {Post-resuscitation care},
	volume = {58},
	issn = {0037-5675},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5523091/},
	doi = {10.11622/smedj.2017060},
	abstract = {Following return of spontaneous circulation (ROSC) after cardiac arrest, the challenge is to institute measures that ensure a higher likelihood of neurologically intact survival. Regardless of the cause of collapse, multiple organ systems may be affected secondary to post-cardiac arrest syndrome. Interventions required for post-ROSC care are bundled into a care regimen: prompt identification and treatment of the cause of cardiac arrest; and treatment of electrolyte abnormalities. It is also essential to establish definitive airway management to maintain normocapnic ventilation, prevent hyperoxia, and optimise haemodynamic management via judicious intravenous fluids and vasoactive drugs. Targeted temperature management after ROSC confers neuroprotection and leads to improved neurological outcomes. Glycaemic control of blood glucose levels at 6–10 mmol/L, adequate seizure management and measures to optimise neurological functions should be integrated into the care bundle. The interventions outlined can potentially lead to more patients being discharged from hospital alive with good neurological function.},
	number = {7},
	urldate = {2025-03-17},
	journal = {Singapore Medical Journal},
	author = {Pothiawala, Sohil},
	month = jul,
	year = {2017},
	pmid = {28741014},
	pmcid = {PMC5523091},
	pages = {404--407},
}

@article{liu_prediction_2020-1,
	title = {Prediction of {ROSC} {After} {Cardiac} {Arrest} {Using} {Machine} {Learning}},
	volume = {270},
	issn = {1879-8365},
	doi = {10.3233/SHTI200440},
	abstract = {Out-of-hospital cardiac arrest (OHCA) is an important public health problem, with very low survival rate. In treating OHCA patients, the return of spontaneous circulation (ROSC) represents the success of early resuscitation efforts. In this study, we developed a machine learning model to predict ROSC and compared it with the ROSC after cardiac arrest (RACA) score. Results demonstrated the usefulness of machine learning in deriving predictive models.},
	language = {eng},
	journal = {Studies in Health Technology and Informatics},
	author = {Liu, Nan and Ho, Andrew Fu Wah and Pek, Pin Pin and Lu, Tsung-Chien and Khruekarnchana, Pairoj and Song, Kyoung Jun and Tanaka, Hideharu and Naroo, Ghulam Yasin and Gan, Han Nee and Koh, Zhi Xiong and Ma, Huei-Ming and Ong, Marcus},
	month = jun,
	year = {2020},
	pmid = {32570657},
	keywords = {Cardiopulmonary Resuscitation, Emergency Medical Services, Humans, Machine Learning, Out-of-Hospital Cardiac Arrest, Out-of-hospital cardiac arrest, Physiological Phenomena, ROSC, Retrospective Studies, Survival Rate, machine learning, random forest},
	pages = {1357--1358},
}

@article{anderson_left_2017,
	title = {Left {Ventricular} {Compressions} {Improve} {Hemodynamics} in a {Swine} {Model} of {Out}-of-{Hospital} {Cardiac} {Arrest}},
	copyright = {© 2017 National Association of EMS Physicians},
	issn = {1090-3127},
	url = {https://www.tandfonline.com/doi/abs/10.1080/10903127.2016.1241328},
	abstract = {Introduction: We hypothesized that chest compressions located directly over the left ventricle (LV) would improve hemodynamics, including coronary perfusion pressure (CPP), and return of spontaneou...},
	language = {EN},
	urldate = {2025-03-13},
	journal = {Prehospital Emergency Care},
	author = {Anderson, Kenton L. and Castaneda, Maria G. and Boudreau, Susan M. and Sharon, Danny J. and Bebarta, Vikhyat S.},
	month = mar,
	year = {2017},
	note = {Publisher: Taylor \& Francis},
}

@article{reynolds_tissue_2013,
	title = {Tissue oximetry by near-infrared spectroscopy in a porcine model of out-of-hospital cardiac arrest and resuscitation},
	volume = {84},
	issn = {0300-9572},
	url = {https://www.sciencedirect.com/science/article/pii/S0300957212009367},
	doi = {10.1016/j.resuscitation.2012.11.031},
	abstract = {Introduction
Monitoring during resuscitation remains relatively crude. Near-infrared spectroscopy (NIRS) measures aggregate oxygen saturation in a volume of tissue. We assessed the utility of continuous StO2 measurement in a porcine model of cardiac arrest, and explored the effects of differential vasoconstriction on StO2. We hypothesized that (1) StO2 trends correspond with the onset of loss of pulses, resuscitation, and return of spontaneous circulation (ROSC); (2) epinephrine has a dose-dependent effect on StO2.
Methods
We anesthetized and instrumented 7 female swine, placing a NIRS probe on the left forelimb to recorded StO2. After 8min of untreated VF and 2min of CPR, we randomized animals to 0.015mgkg−1 (SDE) or 0.1mgkg−1 (HDE) epinephrine. After 3min of CPR, animals were defibrillated. Animals with ROSC were given SDE, then HDE for subsequent hemodynamic deteriorations. Data were analyzed with descriptive statistics and generalized linear model (alpha=0.05) to determine overall slope of pooled StO2 across animals for resuscitation segments.
Results
Four animals received HDE and three SDE. All achieved ROSC. Significant coefficients (ΔStO2min−1) were noted for resuscitation segments. StO2 decreased after loss of pulses (−29.1; 95\%CI −33.4, −24.7; p{\textless}0.01) but plateaued during CPR (−0.2; 95\%CI −1.2, 0.8; p=0.71). There was a graded decline in StO2 between SDE (−1.3; 95\%CI −1.5, −1.2; p{\textless}0.01) and HDE (−3.1; 95\%CI −5.8, −0.4; p=0.03). The slowest change occurred with ROSC (0.4; 95\%CI 0.3, 0.5; p{\textless}0.01).
Conclusions
In a porcine model of OHCA, peripheral StO2 rapidly decreased after loss of pulses, but did not improve with CPR or epinephrine. It increased extremely slowly after ROSC.},
	number = {6},
	urldate = {2025-03-13},
	journal = {Resuscitation},
	author = {Reynolds, Joshua C. and Salcido, David and Koller, Allison C. and Sundermann, Matthew L. and Frisch, Adam and Suffoletto, Brian P. and Menegazzi, James J.},
	month = jun,
	year = {2013},
	keywords = {CPR, Heart arrest, Near infrared spectroscopy, Oxygenation, Resuscitation, Tissue oximetry},
	pages = {843--847},
}

@misc{richter_cerebral_2021,
	title = {Cerebral oxygenation monitoring in patients during and after cardiac arrest -- a narrative review of current methods and evidence},
	url = {https://www.signavitae.com/articles/10.22514/sv.2021.141},
	abstract = {Hypoxic-ischemic brain injury (HIBI) is a leading cause of mortality in post-cardiac arrest (post-CA) patients who successfully survive the initial cardiopulmonary resuscitation (CPR) but later die in the Intensive Care Unit (ICU). Therefore, a key priority of post-resuscitation ICU care is to prevent and limit the impact of HIBI by optimizing the balance between cerebral oxygen delivery and demand. Traditionally, an optimal systemic oxygen balance is considered to ensure the brain’s oxygen balance. However, the validity of this assumption is uncertain, as the brain constitutes only 2\% of the body mass while accounting for approximately 20\% of basal oxygen consumption at rest. Hence, there is a real need to monitor cerebral oxygenation realistically. Several imaging and bedside monitoring methods are available for cerebral oxygenation monitoring in post-CA patients. Unfortunately, each of them has its limitations. Imaging methods require transporting a critically ill unstable patient to the scanner. Moreover, they provide an assessment of the oxygenation state only at a particular moment, while brain oxygenation is dynamic. Bedside methods, specifically near-infrared spectroscopy (NIRS), brain tissue oxygen tension (PbtO2), and jugular venous oxygen saturation monitoring (SjvO2), have not often been used in studies involving post-CA patients. Hence there is ambiguity regarding clear recommendations for using these bedside monitors. Presently, the most promising option seems to be using the NIRS as an indicator of effective CPR. We present a narrative review focusing on bedside methods and discuss the evidence for their use in adult patients after cardiac arrest.},
	language = {en-US},
	urldate = {2025-03-13},
	author = {Richter, Jaromir and Maca, Jan and Chatterjee, Nilay and Sklienka, Peter and Setra, Adarsh Eshappa and Zahorec, Roman},
	month = nov,
	year = {2021},
	doi = {10.22514/sv.2021.141},
}

@article{tosh_cerebral_2016,
	title = {Cerebral oximetry},
	volume = {16},
	issn = {2058-5349},
	url = {https://dx.doi.org/10.1093/bjaed/mkw024},
	doi = {10.1093/bjaed/mkw024},
	abstract = {The maintenance of adequate oxygen delivery to tissues and organs, especially the brain, is a fundamental objective of the anaesthetic process. The dangers},
	language = {en},
	number = {12},
	urldate = {2025-03-13},
	journal = {BJA Education},
	author = {Tosh, W. and Patteril, M.},
	month = dec,
	year = {2016},
	note = {Publisher: Oxford Academic},
	pages = {417--421},
}

@article{suresh_association_2024,
	title = {The association of regional cerebral oximetry and neurologically intact survival in a porcine model of cardiac arrest},
	volume = {17},
	issn = {2666-5204},
	url = {https://www.sciencedirect.com/science/article/pii/S2666520423001820},
	doi = {10.1016/j.resplu.2023.100539},
	abstract = {Background
The objective of this study was to determine if regional cerebral oximetry (rSO2) assessed during CPR would be predictive of survival with favorable neurological function in a prolonged model of porcine cardiac arrest. This study also examined the relative predictive value of rSO2 and end-tidal carbon dioxide (ETCO2), separately and together.
Methods
This study is a post-hoc analysis of data from a previously published study that compared conventional CPR (C-CPR) and automated head-up positioning CPR (AHUP-CPR). Following 10 min of untreated ventricular fibrillation, 14 pigs were treated with either C-CPR (C-CPR) or AHUP-CPR. rSO2, ETCO2, and other hemodynamic parameters were measured continuously. Pigs were defibrillated after 19 min of CPR. Neurological function was assessed 24 h later.
Results
There were 7 pigs in the neurologically intact group and 7 pigs in the poor outcomes group. Within 6 min of starting CPR, the mean difference in rSO2 by 95\% confidence intervals between the groups became statistically significant (p {\textless} 0.05). The receiver operating curve for rSO2 to predict survival with favorable neurological function reached a maximal area under the curve value after 6 min of CPR (1.0). The correlation coefficient between rSO2 and ETCO2 during CPR increased towards 1.0 over time. The combined predictive value of both parameters was similar to either parameter alone.
Conclusion
Significantly higher rSO2 values were observed within less than 6 min after starting CPR in the pigs that survived versus those that died. rSO2 values were highly predictive of survival with favorable neurological function.},
	urldate = {2025-03-12},
	journal = {Resuscitation Plus},
	author = {Suresh, Mithun and Arango, Susana and Moore, Johanna and Salverda, Bayert and Lick, Michael and Rojas-Salvador, Carolina and Metzger, Anja and Debaty, Guillaume and Pourzand, Pouria and Kaizer, Alexander and Lurie, Keith},
	month = mar,
	year = {2024},
	keywords = {Active compression-decompression, Automated head-up positioning CPR, Cardiac arrest, Cardiopulmonary resuscitation, Cerebral oximetry, Impedance threshold device},
	pages = {100539},
}

@article{kim_development_2024,
	title = {Development of artificial intelligence-driven biosignal-sensitive cardiopulmonary resuscitation robot},
	volume = {202},
	issn = {0300-9572},
	url = {https://www.sciencedirect.com/science/article/pii/S030095722400248X},
	doi = {10.1016/j.resuscitation.2024.110354},
	abstract = {Aim of the study
We evaluated whether an artificial intelligence (AI)-driven robot cardiopulmonary resuscitation (CPR) could improve hemodynamic parameters and clinical outcomes.
Methods
We developed an AI-driven CPR robot which utilizes an integrated feedback system with an AI model predicting carotid blood flow (CBF). Twelve pigs were assigned to the AI robot group (n = 6) and the LUCAS 3 group (n = 6). They underwent 6 min of CPR after 7 min of ventricular fibrillation. In the AI robot group, the robot explored for the optimal compression position, depth and rate during the first 270-second period, and continued CPR with the optimal setup during the next 90-second period and beyond. The primary outcome was CBF during the last 90-second period. The secondary outcomes were coronary perfusion pressure (CPP), end-tidal carbon dioxide level (ETCO2) and return of spontaneous circulation (ROSC).
Results
The AI model’s prediction performance was excellent (Pearson correlation coefficient = 0.98). CBF did not differ between the two groups [estimate and standard error (SE), −23.210 ± 20.193, P = 0.250]. CPP, ETCO2 level and rate of ROSC also did not show difference [estimate and SE, −0.214 ± 7.245, P = 0.976 for CPP; estimate and SE, 1.745 ± 3.199, P = 0.585 for ETCO2; 5/6 (83.3\%) vs. 4/6 (66.7\%), P = 1.000 for ROSC).
Conclusion
This study provides proof of concept that an AI-driven CPR robot in porcine cardiac arrest is feasible. Compared to a LUCAS 3, an AI-driven CPR robot produced comparable hemodynamic and clinical outcomes.},
	urldate = {2025-03-12},
	journal = {Resuscitation},
	author = {Kim, Taegyun and Suh, Gil Joon and Kim, Kyung Su and Kim, Hayoung and Park, Heesu and Kwon, Woon Yong and Park, Jaeheung and Sim, Jaehoon and Hur, Sungmoon and Lee, Jung Chan and Shin, Dong Ah and Cho, Woo Sang and Kim, Byung Jun and Kwon, Soyoon and Lee, Ye Ji},
	month = sep,
	year = {2024},
	keywords = {Artificial intelligence, Biosignal, Cardiopulmonary Resuscitation, Robot},
	pages = {110354},
}

@article{jiang_estimation_2024,
	title = {Estimation of invasive coronary perfusion pressure using electrocardiogram and {Photoplethysmography} in a porcine model of cardiac arrest},
	volume = {254},
	issn = {0169-2607},
	url = {https://www.sciencedirect.com/science/article/pii/S0169260724002797},
	doi = {10.1016/j.cmpb.2024.108284},
	abstract = {Background
Coronary perfusion pressure (CPP) indicates spontaneous return of circulation and is recommended for high-quality cardiopulmonary resuscitation (CPR). This study aimed to investigate a method for non-invasive estimation of CPP using electrocardiography (ECG) and photoplethysmography (PPG) during CPR.
Methods
Nine pigs were used in this study. ECG, PPG, invasive arterial blood pressure (ABP), and right atrial pressure (RAP) signals were simultaneously recorded. The CPPs were estimated using three datasets: (a) ECG, (b) PPG, and (c) ECG and PPG, and were compared with invasively measured CPPs. Four machine-learning algorithms, namely support vector regression, random forest (RF), K-nearest neighbor, and gradient-boosted regression tree, were used for estimation of CPP.
Results
The RF model with a combined ECG and PPG dataset achieved better estimation of CPP than the other algorithms. Specifically, the mean absolute error was 4.49 mmHg, the root mean square error was 6.15 mm Hg, and the adjusted R2 was 0.75. A strong correlation was found between the non-invasive estimation and invasive measurement of CPP (r = 0.88), which supported our hypothesis that machine-learning-based analysis of ECG and PPG parameters can provide a non-invasive estimation of CPP for CPR.
Conclusions
This study proposes a novel estimation of CPP using ECG and PPG with machine-learning-based algorithms. Non-invasively estimated CPP showed a high correlation with invasively measured CPP and may serve as an easy-to-use physiological indicator for high-quality CPR treatment.},
	urldate = {2025-03-12},
	journal = {Computer Methods and Programs in Biomedicine},
	author = {Jiang, Lijun and Chen, Shuxin and Pan, Xin and Zhang, Jingyu and Yin, Xinxin and Guo, Chang and Sun, Mingze and Ding, Bozhi and Zhai, Xiaoxuan and Li, Ke and Wang, Jiali and Chen, Yuguo},
	month = sep,
	year = {2024},
	keywords = {Cardiac arrest, Cardiopulmonary resuscitation, Coronary perfusion pressure, Machine learning, Waveform analysis},
	pages = {108284},
}

@article{skrifvars_optimal_2023,
	title = {Optimal oxygen and mean arterial blood pressure targets after cardiac arrest},
	volume = {49},
	issn = {0342-4642},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10029779/},
	doi = {10.1007/s00134-023-07018-7},
	number = {5},
	urldate = {2025-03-12},
	journal = {Intensive Care Medicine},
	author = {Skrifvars, Markus B and Bray, Janet and Hassager, Christian},
	year = {2023},
	pmid = {36943441},
	pmcid = {PMC10029779},
	pages = {569--571},
}

@article{oglat_review_2018,
	title = {A {Review} of {Medical} {Doppler} {Ultrasonography} of {Blood} {Flow} in {General} and {Especially} in {Common} {Carotid} {Artery}},
	volume = {26},
	issn = {0929-6441},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029191/},
	doi = {10.4103/JMU.JMU_11_17},
	abstract = {Medical Doppler ultrasound is usually utilized in the clinical adjusting to evaluate and estimate blood flow in both the major (large) and the minor (tiny) vessels of the body. The normal and abnormal sign waveforms can be shown by spectral Doppler technique. The sign waveform is individual to each vessel. Thus, it is significant for the operator and the clinicians to understand the normal and abnormal diagnostic in a spectral Doppler show. The aim of this review is to explain the physical principles behind the medical Doppler ultrasound, also, to use some of the mathematical formulas utilized in the medical Doppler ultrasound examination. Furthermore, we discussed the color and spectral flow model of Doppler ultrasound. Finally, we explained spectral Doppler sign waveforms to show both the normal and abnormal signs waveforms that are individual to the common carotid artery, because these signs are important for both the radiologist and sonographer to perceive both the normal and abnormal in a spectral Doppler show.},
	number = {1},
	urldate = {2025-03-12},
	journal = {Journal of Medical Ultrasound},
	author = {Oglat, Ammar A. and Matjafri, M. Z. and Suardi, Nursakinah and Oqlat, Mohammad A. and Abdelrahman, Mostafa A. and Oqlat, Ahmad A.},
	year = {2018},
	pmid = {30065507},
	pmcid = {PMC6029191},
	pages = {3--13},
}

@article{nolan_european_2021,
	title = {European {Resuscitation} {Council} and {European} {Society} of {Intensive} {Care} {Medicine} guidelines 2021: post-resuscitation care},
	volume = {47},
	issn = {1432-1238},
	shorttitle = {European {Resuscitation} {Council} and {European} {Society} of {Intensive} {Care} {Medicine} guidelines 2021},
	url = {https://doi.org/10.1007/s00134-021-06368-4},
	doi = {10.1007/s00134-021-06368-4},
	abstract = {The European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) have collaborated to produce these post-resuscitation care guidelines for adults, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include the post-cardiac arrest syndrome, diagnosis of cause of cardiac arrest, control of oxygenation and ventilation, coronary reperfusion, haemodynamic monitoring and management, control of seizures, temperature control, general intensive care management, prognostication, long-term outcome, rehabilitation and organ donation.},
	language = {en},
	number = {4},
	urldate = {2025-03-12},
	journal = {Intensive Care Medicine},
	author = {Nolan, Jerry P. and Sandroni, Claudio and Böttiger, Bernd W. and Cariou, Alain and Cronberg, Tobias and Friberg, Hans and Genbrugge, Cornelia and Haywood, Kirstie and Lilja, Gisela and Moulaert, Véronique R. M. and Nikolaou, Nikolaos and Olasveengen, Theresa Mariero and Skrifvars, Markus B. and Taccone, Fabio and Soar, Jasmeet},
	month = apr,
	year = {2021},
	keywords = {Cardiac arrest, Guidelines, Post resuscitation care, Prognostication},
	pages = {369--421},
}

@article{yamamoto_early_2023,
	title = {Early restricted oxygen therapy after resuscitation from cardiac arrest ({ER}-{OXYTRAC}): protocol for a stepped-wedge cluster randomised controlled trial},
	volume = {13},
	issn = {2044-6055},
	shorttitle = {Early restricted oxygen therapy after resuscitation from cardiac arrest ({ER}-{OXYTRAC})},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10510872/},
	doi = {10.1136/bmjopen-2023-074475},
	abstract = {Introduction
Cardiac arrest is a critical condition, and patients often experience postcardiac arrest syndrome (PCAS) even after the return of spontaneous circulation (ROSC). Administering a restricted amount of oxygen in the early phase after ROSC has been suggested as a potential therapy for PCAS; however, the optimal target for arterial partial pressure of oxygen or peripheral oxygen saturation (SpO2) to safely and effectively reduce oxygen remains unclear. Therefore, we aimed to validate the efficacy of restricted oxygen treatment with 94\%–95\% of the target SpO2 during the initial 12 hours after ROSC for patients with PCAS.

Methods and analysis
ER-OXYTRAC (early restricted oxygen therapy after resuscitation from cardiac arrest) is a nationwide, multicentre, pragmatic, single-blind, stepped-wedge cluster randomised controlled trial targeting cases of non-traumatic cardiac arrest. This study includes adult patients with out-of-hospital or in-hospital cardiac arrest who achieved ROSC in 39 tertiary centres across Japan, with a target sample size of 1000. Patients whose circulation has returned before hospital arrival and those with cardiac arrest due to intracranial disease or intoxication are excluded. Study participants are assigned to either the restricted oxygen (titration of a fraction of inspired oxygen with 94\%–95\% of the target SpO2) or the control (98\%–100\% of the target SpO2) group based on cluster randomisation per institution. The trial intervention continues until 12 hours after ROSC. Other treatments for PCAS, including oxygen administration later than 12 hours, can be determined by the treating physicians. The primary outcome is favourable neurological function, defined as cerebral performance category 1–2 at 90 days after ROSC, to be compared using an intention-to-treat analysis.

Ethics and dissemination
This study has been approved by the Institutional Review Board at Keio University School of Medicine (approval number: 20211106). Written informed consent will be obtained from all participants or their legal representatives. Results will be disseminated via publications and presentations.

Trial registration number
UMIN Clinical Trials Registry (UMIN000046914).},
	number = {9},
	urldate = {2025-03-12},
	journal = {BMJ Open},
	author = {Yamamoto, Ryo and Yamakawa, Kazuma and Endo, Akira and Homma, Koichiro and Sato, Yasunori and Takemura, Ryo and Yamagiwa, Takeshi and Shimizu, Keiki and Kaito, Daiki and Yagi, Masayuki and Yonemura, Taku and Shibusawa, Takayuki and Suzuki, Ginga and Shoji, Takahiro and Miura, Naoya and Takahashi, Jiro and Narita, Chihiro and Kurata, Saori and Minami, Kazunobu and Wada, Takeshi and Fujinami, Yoshihisa and Tsubouchi, Yohei and Natsukawa, Mai and Nagayama, Jun and Takayama, Wataru and Ishikura, Ken and Yokokawa, Kyoko and Fujita, Yasuo and Nakayama, Hirofumi and Tokuyama, Hideki and Shinada, Kota and Taira, Takayuki and Fukui, Shoki and Ushio, Noritaka and Nakane, Masaki and Hoshiyama, Eisei and Tampo, Akihito and Sageshima, Hisako and Takami, Hiroki and Iizuka, Shinichi and Kikuchi, Hitoshi and Hagiwara, Jun and Tagami, Takashi and Funato, Yumi and Sasaki, Junichi and ER-OXYTRAC, study group},
	month = sep,
	year = {2023},
	pmid = {37714682},
	pmcid = {PMC10510872},
	pages = {e074475},
}

@article{koch_carotid_2022,
	title = {Carotid {Artery} {Ultrasound} in the (peri-) {Arrest} {Setting}—{A} {Prospective} {Pilot} {Study}},
	volume = {11},
	issn = {2077-0383},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8780199/},
	doi = {10.3390/jcm11020469},
	abstract = {Point-of-care ultrasounds (US) are used during cardiopulmonary resuscitation (CPR) and after return of spontaneous circulation (ROSC). Carotid ultrasounds are a potential non-invasive monitoring tool for chest compressions, but their general value and feasibility during CPR are not fully determined. In this prospective observational study, we performed carotid US during conventional- and extracorporeal CPR and after ROSC with at least one transverse and coronal image, corresponding loops with and without color doppler, and pulsed-wave doppler loops. The feasibility of carotid US during (peri-)arrest and type and frequency of diagnostic findings were examined. We recruited 16 patients and recorded utilizable US images in 14 cases (88\%; complete imaging protocols in 11 patients [69\%]). In three of all patients (19\%) and in 60\% (3/5) of cases during CPR plus a full imaging protocol, we observed: (i) in one patient a collapse of the common carotid artery linked to hypovolemia, and (ii) in two patients a biphasic flow during CPR linked to prolonged low-flow time prior to admission and adverse outcome. Carotid artery morphology and carotid blood flow characteristics may serve as therapeutic target and prognostic parameters. However, future studies with larger sample sizes are needed.},
	number = {2},
	urldate = {2025-03-11},
	journal = {Journal of Clinical Medicine},
	author = {Koch, Moritz and Mueller, Matthias and Warenits, Alexandra-Maria and Holzer, Michael and Spiel, Alexander and Schnaubelt, Sebastian},
	month = jan,
	year = {2022},
	pmid = {35054163},
	pmcid = {PMC8780199},
	pages = {469},
}

@article{gaspari_femoral_2023,
	title = {Femoral {Arterial} {Doppler} {Use} {During} {Active} {Cardiopulmonary} {Resuscitation}},
	volume = {81},
	issn = {0196-0644},
	url = {https://www.sciencedirect.com/science/article/pii/S0196064422013269},
	doi = {10.1016/j.annemergmed.2022.12.002},
	abstract = {Study Objective
This study explored femoral arterial Doppler during active cardiopulmonary resuscitation (CPR) to identify and characterize the resumptions of cardiac activity without stopping CPR.
Methods
This was a proof-of-concept study exploring arterial Doppler during cardiac arrest. Patients in cardiac arrest undergoing active CPR were prospectively enrolled. Arterial Doppler of the common femoral artery was recorded during CPR and during pauses in CPR. CPR-induced arterial tracings and native cardiac-induced tracings were analyzed for rate and peak systolic velocity. Cardiac activity on echocardiogram during pause in CPR was classified as “absent,” “disorganized,” or “organized.” Descriptive data and survival are presented as mean and 95\% confidence intervals (CI), as well as sensitivity and specificity of Doppler during active CPR in detecting native cardiac pulsations.
Results
Sixteen patients with 48 paired Doppler recordings during active CPR, pause in CPR, and associated echocardiogram were enrolled. Native cardiac-induced tracings were visible during 39.6\% of pauses in CPR (19 of 48) and during 18.8\% of the periods of active CPR (9 of 48). Arterial pulsations were more frequently visualized with organized contractions by echocardiogram (10 of 14, 71\%) than disorganized contractions (9 of 22, 41\%). Arterial Doppler was 100\% specific and 50\% sensitive in detecting organized cardiac activity during active CPR. Patients with visible native cardiac pulsations during active CPR demonstrated 0\% mortality compared with 67\% mortality without visible arterial pulsations.
Conclusion
Arterial Doppler tracings may identify the resumption of native cardiac activity during active CPR; however, more research is needed.},
	number = {5},
	urldate = {2024-10-10},
	journal = {Annals of Emergency Medicine},
	author = {Gaspari, Romolo J. and Lindsay, Robert and Dowd, Andrew and Gleeson, Timothy},
	month = may,
	year = {2023},
	pages = {523--531},
}

@article{ahn_artificial_2024,
	title = {Artificial intelligence for predicting shockable rhythm during cardiopulmonary resuscitation: {In}-hospital setting},
	volume = {202},
	issn = {0300-9572},
	shorttitle = {Artificial intelligence for predicting shockable rhythm during cardiopulmonary resuscitation},
	url = {https://www.sciencedirect.com/science/article/pii/S0300957224002193},
	doi = {10.1016/j.resuscitation.2024.110325},
	abstract = {Aim of the study
This study aimed to develop an artificial intelligence (AI) model capable of predicting shockable rhythms from electrocardiograms (ECGs) with compression artifacts using real-world data from emergency department (ED) settings. Additionally, we aimed to explore the black box nature of AI models, providing explainability.
Methods
This study is retrospective, observational study using a prospectively collected database. Adult patients who presented to the ED with cardiac arrest or experienced cardiac arrest in the ED between September 2021 and February 2024 were included. ECGs with a compression artifact of 5 s before every rhythm check were used for analysis. The AI model was designed based on convolutional neural networks. The ECG data were assigned into training, validation, and testing sets on a per-patient basis to ensure that ECGs from the same patient did not appear in multiple sets. Gradient-weighted class activation mapping was employed to demonstrate AI explainability.
Results
A total of 1,889 ECGs with compression artifacts from 172 patients were used. The area under the receiver operating characteristic curve (AUROC) for shockable rhythm prediction was 0.8672 (95\% confidence interval [CI]: 0.8161–0.9122). The AUROCs for manual and mechanical compression were 0.8771 (95\% CI: 0.8054–0.9408) and 0.8466 (95\% CI: 0.7630–0.9138), respectively.
Conclusion
This study was the first to accurately predict shockable rhythms during compression using an AI model trained with actual patient ECGs recorded during resuscitation. Furthermore, we demonstrated the explainability of the AI. This model can minimize interruption of cardiopulmonary resuscitation and potentially lead to improved outcomes.},
	urldate = {2024-10-10},
	journal = {Resuscitation},
	author = {Ahn, Sejoong and Jung, Sumin and Park, Jong-Hak and Cho, Hanjin and Moon, Sungwoo and Lee, Sukyo},
	month = sep,
	year = {2024},
	keywords = {Artificial intelligence, Cardio-pulmonary resuscitation, Electrocardiogram},
	pages = {110325},
}

@article{selvaraju_grad-cam_2020,
	title = {Grad-{CAM}: {Visual} {Explanations} from {Deep} {Networks} via {Gradient}-based {Localization}},
	volume = {128},
	issn = {0920-5691, 1573-1405},
	shorttitle = {Grad-{CAM}},
	url = {http://arxiv.org/abs/1610.02391},
	doi = {10.1007/s11263-019-01228-7},
	abstract = {We propose a technique for producing "visual explanations" for decisions from a large class of CNN-based models, making them more transparent. Our approach - Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept, flowing into the final convolutional layer to produce a coarse localization map highlighting important regions in the image for predicting the concept. Grad-CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers, (2) CNNs used for structured outputs, (3) CNNs used in tasks with multimodal inputs or reinforcement learning, without any architectural changes or re-training. We combine Grad-CAM with fine-grained visualizations to create a high-resolution class-discriminative visualization and apply it to off-the-shelf image classification, captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into their failure modes, (b) are robust to adversarial images, (c) outperform previous methods on localization, (d) are more faithful to the underlying model and (e) help achieve generalization by identifying dataset bias. For captioning and VQA, we show that even non-attention based models can localize inputs. We devise a way to identify important neurons through Grad-CAM and combine it with neuron names to provide textual explanations for model decisions. Finally, we design and conduct human studies to measure if Grad-CAM helps users establish appropriate trust in predictions from models and show that Grad-CAM helps untrained users successfully discern a 'stronger' nodel from a 'weaker' one even when both make identical predictions. Our code is available at https://github.com/ramprs/grad-cam/, along with a demo at http://gradcam.cloudcv.org, and a video at youtu.be/COjUB9Izk6E.},
	number = {2},
	urldate = {2024-10-07},
	journal = {International Journal of Computer Vision},
	author = {Selvaraju, Ramprasaath R. and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
	month = feb,
	year = {2020},
	note = {arXiv:1610.02391 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	pages = {336--359},
}

@misc{gilpin_explaining_2019,
	title = {Explaining {Explanations}: {An} {Overview} of {Interpretability} of {Machine} {Learning}},
	shorttitle = {Explaining {Explanations}},
	url = {http://arxiv.org/abs/1806.00069},
	doi = {10.48550/arXiv.1806.00069},
	abstract = {There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions in some level of detail. These explanations are important to ensure algorithmic fairness, identify potential bias/problems in the training data, and to ensure that the algorithms perform as expected. However, explanations produced by these systems is neither standardized nor systematically assessed. In an effort to create best practices and identify open challenges, we provide our definition of explainability and show how it can be used to classify existing literature. We discuss why current approaches to explanatory methods especially for deep neural networks are insufficient. Finally, based on our survey, we conclude with suggested future research directions for explanatory artificial intelligence.},
	urldate = {2024-10-07},
	publisher = {arXiv},
	author = {Gilpin, Leilani H. and Bau, David and Yuan, Ben Z. and Bajwa, Ayesha and Specter, Michael and Kagal, Lalana},
	month = feb,
	year = {2019},
	note = {arXiv:1806.00069 [cs, stat]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{samek_explainable_2017,
	title = {Explainable {Artificial} {Intelligence}: {Understanding}, {Visualizing} and {Interpreting} {Deep} {Learning} {Models}},
	shorttitle = {Explainable {Artificial} {Intelligence}},
	url = {http://arxiv.org/abs/1708.08296},
	doi = {10.48550/arXiv.1708.08296},
	abstract = {With the availability of large databases and recent improvements in deep learning methodology, the performance of AI systems is reaching or even exceeding the human level on an increasing number of complex tasks. Impressive examples of this development can be found in domains such as image classification, sentiment analysis, speech understanding or strategic game playing. However, because of their nested non-linear structure, these highly successful machine learning and artificial intelligence models are usually applied in a black box manner, i.e., no information is provided about what exactly makes them arrive at their predictions. Since this lack of transparency can be a major drawback, e.g., in medical applications, the development of methods for visualizing, explaining and interpreting deep learning models has recently attracted increasing attention. This paper summarizes recent developments in this field and makes a plea for more interpretability in artificial intelligence. Furthermore, it presents two approaches to explaining predictions of deep learning models, one method which computes the sensitivity of the prediction with respect to changes in the input and one approach which meaningfully decomposes the decision in terms of the input variables. These methods are evaluated on three classification tasks.},
	urldate = {2024-10-07},
	publisher = {arXiv},
	author = {Samek, Wojciech and Wiegand, Thomas and Müller, Klaus-Robert},
	month = aug,
	year = {2017},
	note = {arXiv:1708.08296 [cs, stat]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning},
}

@article{jahren_estimation_2020,
	title = {Estimation of {End}-{Diastole} in {Cardiac} {Spectral} {Doppler} {Using} {Deep} {Learning}},
	volume = {67},
	issn = {1525-8955},
	url = {https://ieeexplore.ieee.org/abstract/document/9094189},
	doi = {10.1109/TUFFC.2020.2995118},
	abstract = {Electrocardiogram (ECG) is often used together with a spectral Doppler ultrasound to separate heart cycles by determining the end-diastole locations. However, the ECG signal is not always recorded. In such cases, the cardiac cycles can be estimated manually from the ultrasound data retrospectively. We present a deep learning-based method for automatic detection of the end-diastoles in spectral Doppler spectrograms. The method uses a combination of a convolutional neural network (CNN) for extracting features and a recurrent neural network (RNN) for modeling temporal relations. In echocardiography, there are three Doppler spectrogram modalities, continuous wave, pulsed wave, and tissue velocity Doppler. Both the training and test data sets include all three modalities. The model was tested on 643 spectrograms coming from different hospitals than in the training data set. For the purposes described in this work, a valid end-diastole detection is defined as a prediction being closer than 60 ms to the reference value. We will refer to these as true detections. Similarly, a prediction farther away is defined as nonvalid or false detections. The method automatically rejects spectrograms where the detection of an end-diastole has low confidence. When setting the algorithm to reject 1.9\%, the method achieved 97.7\% true detections with a mean error of 14 ms and had 2.5\% false detections on the remaining spectrograms.},
	number = {12},
	urldate = {2024-10-01},
	journal = {IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control},
	author = {Jahren, Tollef Struksnes and Steen, Erik N. and Aase, Svein Arne and Solberg, Anne H. Schistad},
	month = dec,
	year = {2020},
	note = {Conference Name: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control},
	keywords = {Deep learning, Doppler effect, Electrocardiography, Heart, Machine learning, Spectrogram, Ultrasonic imaging, Valves, echocardiography, end-diastole, spectral Doppler},
	pages = {2605--2614},
}

@article{li_false_2023,
	title = {A false peak recognition method based on deep learning},
	volume = {238},
	issn = {0169-7439},
	url = {https://www.sciencedirect.com/science/article/pii/S0169743923000990},
	doi = {10.1016/j.chemolab.2023.104849},
	abstract = {Peak detection is a critical aspect of chemical spectrum data processing. However, existing peak detection methods are susceptible to producing false positive signals, which can complicate interpretation and lead to erroneous scientific findings. Manually detecting false peaks is a time-consuming process. Therefore this paper proposes a novel approach using a multi-scale convolution bi-directional LSTM attention depth network for identifying false peaks. This network leverages the feature extraction capabilities of the CNN network and the contextual connection abilities of LSTM. Our experimental results show that the proposed method achieves an accuracy rate of 89.67\% in detecting false peaks in real LC-MS data, making it a powerful tool for automatic identification of false peaks.},
	urldate = {2024-10-01},
	journal = {Chemometrics and Intelligent Laboratory Systems},
	author = {Li, Kun and Zhang, Yingchao and Li, Yuanlu},
	month = jul,
	year = {2023},
	keywords = {Deep learning, False peak recognition, LC-MS data, Peak detection},
	pages = {104849},
}

@inproceedings{elola_deep_2018,
	title = {Deep {Learning} for {Pulse} {Detection} in {Out}-of-{Hospital} {Cardiac} {Arrest} {Using} the {ECG}},
	volume = {45},
	url = {https://ieeexplore.ieee.org/abstract/document/8744005},
	doi = {10.22489/CinC.2018.093},
	abstract = {Pulse detection during out-of-hospital cardiac arrest is necessary to identify cardiac arrest and detect return of spontaneous circulation. Currently, carotid pulse checking and checking for signs of life are the most widespread procedures for pulse detection, but both have been proven inaccurate and time consuming. Automatic methods that could be integrated in Automated External Defibrillators (AEDs) are needed. In this study we propose a deep neural network classifier to detect pulse using exclusively the ECG. We extracted 3914 segments of 4s from 279 patients, all of them with an organized rhythm. They were annotated as pulsed rhythm or pulseless rhythm based on clinical information. A total of 2372 pulsed rhythms and 1542 pulseless rhythms were included in the study. To train and test the model 3038 (223 patients) and 876 segments (56 patients) were used, respectively. The model was evaluated in terms of Sensitivity (Se) and Specificity (Sp) for pulse detection. The network showed a Se/Sp of 89.4\%/97.2\% during training process and 91.7\%/92.5\% for the test set.},
	urldate = {2024-09-30},
	booktitle = {2018 {Computing} in {Cardiology} {Conference} ({CinC})},
	author = {Elola, Andoni and Aramendi, Elisabete and Irusta, Unai and Picón, Artzai and Alonso, Erik and Owens, Pamela and Idris, Ahamed},
	month = sep,
	year = {2018},
	note = {ISSN: 2325-887X},
	keywords = {Cardiac arrest, Deep learning, Electrocardiography, Gaussian noise, Impedance, Rhythm, Training},
	pages = {1--4},
}

@article{park_artificial_2024,
	title = {Artificial intelligence-based evaluation of carotid artery compressibility via point-of-care ultrasound in determining the return of spontaneous circulation during cardiopulmonary resuscitation},
	volume = {202},
	issn = {0300-9572},
	url = {https://www.sciencedirect.com/science/article/pii/S0300957224001965},
	doi = {10.1016/j.resuscitation.2024.110302},
	abstract = {Aim
This study introduces RealCAC-Net, an artificial intelligence (AI) system, to quantify carotid artery compressibility (CAC) and determine the return of spontaneous circulation (ROSC) during cardiopulmonary resuscitation.
Methods
A prospective study based on data from a South Korean emergency department from 2022 to 2023 investigated carotid artery compressibility in adult patients with cardiac arrest using a novel AI model, RealCAC-Net. The data comprised 11,958 training images from 161 cases and 15,080 test images from 134 cases. RealCAC-Net processes images in three steps: TransUNet-based segmentation, the carotid artery compressibility measurement algorithm for improved segmentation and CAC calculation, and CAC-based classification from 0 (indicating a circular shape) to 1 (indicating high compression). The accuracy of the ROSC classification model was tested using metrics such as the dice similarity coefficient, intersection-over-union, precision, recall, and F1 score.
Results
RealCAC-Net, which applied the carotid artery compressibility measurement algorithm, performed better than the baseline model in cross-validation, with an average dice similarity coefficient of 0.90, an intersection-over-union of 0.84, and a classification accuracy of 0.96. The test set achieved a classification accuracy of 0.96 and an F1 score of 0.97, demonstrating its efficacy in accurately identifying ROSC in cardiac arrest situations.
Conclusions
RealCAC-Net enabled precise CAC quantification for ROSC determination during cardiopulmonary resuscitation. Future research should integrate this AI-enhanced ultrasound approach to revolutionize emergency care.},
	urldate = {2024-09-30},
	journal = {Resuscitation},
	author = {Park, Subin and Yoon, Hee and Yeon Kang, Soo and Joon Jo, Ik and Heo, Sejin and Chang, Hansol and Eun Park, Jong and Lee, Guntak and Kim, Taerim and Yeon Hwang, Sung and Park, Soyoung and Jin Chung, Myung},
	month = sep,
	year = {2024},
	keywords = {Artificial intelligence, Cardiopulmonary resuscitation, Carotid artery, Point-of-care ultrasound, Pulse check, Return of spontaneous circulation},
	pages = {110302},
}

@article{dodge_preoperative_2012,
	title = {Preoperative identification of a suspicious adnexal mass: {A} systematic review and meta-analysis},
	volume = {126},
	issn = {0090-8258},
	shorttitle = {Preoperative identification of a suspicious adnexal mass},
	url = {https://www.sciencedirect.com/science/article/pii/S0090825812002491},
	doi = {10.1016/j.ygyno.2012.03.048},
	abstract = {Objective
To systematically review the existing literature in order to determine the optimal strategy for preoperative identification of the adnexal mass suspicious for ovarian cancer.
Methods
A review of all systematic reviews and guidelines published between 1999 and 2009 was conducted as a first step. After the identification of a 2004 AHRQ systematic review on the topic, searches of MEDLINE for studies published since 2004 was also conducted to update and supplement the evidentiary base. A bivariate, random-effects meta-regression model was used to produce summary estimates of sensitivity and specificity and to plot summary ROC curves with 95\% confidence regions.
Results
Four meta-analyses and 53 primary studies were included in this review. The diagnostic performance of each technology was compared and contrasted based on the summary data on sensitivity and specificity obtained from the meta-analysis. Results suggest that 3D ultrasonography has both a higher sensitivity and specificity when compared to 2D ultrasound. Established morphological scoring systems also performed with respectable sensitivity and specificity, each with equivalent diagnostic competence. Explicit scoring systems did not perform as well as other diagnostic testing methods. Assessment of an adnexal mass by colour Doppler technology was neither as sensitive nor as specific as simple ultrasonography. Of the three imaging modalities considered, MRI appeared to perform the best, although results were not statistically different from CT. PET did not perform as well as either MRI or CT. The measurement of the CA-125 tumour marker appears to be less reliable than do other available assessment methods.
Conclusion
The best available evidence was collected and included in this rigorous systematic review and meta-analysis. The abundant evidentiary base provided the context and direction for the diagnosis of early-staged ovarian cancer.},
	number = {1},
	urldate = {2024-09-01},
	journal = {Gynecologic Oncology},
	author = {Dodge, Jason E. and Covens, Allan L. and Lacchetti, Christina and Elit, Laurie M. and Le, Tien and Devries-Aboud, Michaela and Fung-Kee-Fung, Michael},
	month = jul,
	year = {2012},
	keywords = {Adenxal mass, Diagnosis, Ovarian cancer, Sensitivity, Specificity},
	pages = {157--166},
}

@misc{deodato_bayesian_2019,
	title = {Bayesian {Neural} {Networks} for {Cellular} {Image} {Classification} and {Uncertainty} {Analysis}},
	copyright = {© 2019, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/},
	url = {https://www.biorxiv.org/content/10.1101/824862v1},
	doi = {10.1101/824862},
	abstract = {Over the last decades, deep learning models have rapidly gained popularity for their ability to achieve state-of-the-art performances in different inference settings. Deep neural networks have been applied to an increasing number of problems spanning different domains of application. Novel applications define a new set of requirements that transcend accurate predictions and depend on uncertainty measures. The aims of this study are to implement Bayesian neural networks and use the corresponding uncertainty estimates to perform predictions and dataset analysis. We identify two main advantages in modeling the predictive uncertainty of deep neural networks performing classification tasks. The first is the possibility to discard highly uncertain predictions to be able to guarantee a higher accuracy of the remaining predictions. The second is the identification of unfamiliar patterns in the data that correspond to outliers in the model representation of the training data distribution. Such outliers can be further characterized as either corrupted observations or data belonging to different domains. Both advantages are well demonstrated with the benchmark datasets. Furthermore we apply the Bayesian approach to a biomedical imaging dataset where cancer cells are treated with diverse drugs, and show how one can increase classification accuracy and identify noise in the ground truth labels with uncertainty analysis.},
	language = {en},
	urldate = {2024-08-14},
	publisher = {bioRxiv},
	author = {Deodato, Giacomo and Ball, Christopher and Zhang, Xian},
	month = oct,
	year = {2019},
	note = {Pages: 824862
Section: New Results},
}

@inproceedings{wang_gradient-based_2023,
	address = {Vancouver, BC, Canada},
	title = {Gradient-based {Uncertainty} {Attribution} for {Explainable} {Bayesian} {Deep} {Learning}},
	copyright = {https://doi.org/10.15223/policy-029},
	isbn = {9798350301298},
	url = {https://ieeexplore.ieee.org/document/10203792/},
	doi = {10.1109/CVPR52729.2023.01159},
	abstract = {Predictions made by deep learning models are prone to data perturbations, adversarial attacks, and out-ofdistribution inputs. To build a trusted AI system, it is therefore critical to accurately quantify the prediction uncertainties. While current efforts focus on improving uncertainty quantification accuracy and efficiency, there is a need to identify uncertainty sources and take actions to mitigate their effects on predictions. Therefore, we propose to develop explainable and actionable Bayesian deep learning methods to not only perform accurate uncertainty quantification but also explain the uncertainties, identify their sources, and propose strategies to mitigate the uncertainty impacts. Specifically, we introduce a gradient-based uncertainty attribution method to identify the most problematic regions of the input that contribute to the prediction uncertainty. Compared to existing methods, the proposed UABackprop has competitive accuracy, relaxed assumptions, and high efficiency. Moreover, we propose an uncertainty mitigation strategy that leverages the attribution results as attention to further improve the model performance. Both qualitative and quantitative evaluations are conducted to demonstrate the effectiveness of our proposed methods.},
	language = {en},
	urldate = {2024-08-14},
	booktitle = {2023 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	publisher = {IEEE},
	author = {Wang, Hanjing and Joshi, Dhiraj and Wang, Shiqiang and Ji, Qiang},
	month = jun,
	year = {2023},
	pages = {12044--12053},
}

@misc{noauthor_notitle_nodate,
}

@article{arco_uncertainty-driven_2023,
	title = {Uncertainty-driven ensembles of multi-scale deep architectures for image classification},
	volume = {89},
	issn = {1566-2535},
	url = {https://www.sciencedirect.com/science/article/pii/S1566253522001038},
	doi = {10.1016/j.inffus.2022.08.010},
	abstract = {The use of automatic systems for medical image classification has revolutionized the diagnosis of a high number of diseases. These alternatives, which are usually based on artificial intelligence (AI), provide a helpful tool for clinicians, eliminating the inter and intra-observer variability that the diagnostic process entails. Convolutional Neural Network (CNNs) have proved to be an excellent option for this purpose, demonstrating a large performance in a wide range of contexts. However, it is also extremely important to quantify the reliability of the model’s predictions in order to guarantee the confidence in the classification. In this work, we propose a multi-level ensemble classification system based on a Bayesian Deep Learning approach in order to maximize performance while providing the uncertainty of each classification decision. This tool combines the information extracted from different architectures by weighting their results according to the uncertainty of their predictions. Performance is evaluated in a wide range of real scenarios: in the first one, the aim is to differentiate between different pulmonary pathologies: controls vs bacterial pneumonia vs viral pneumonia. A two-level decision tree is employed to divide the 3-class classification into two binary classifications, yielding an accuracy of 98.19\%. In the second context, performance is assessed for the diagnosis of Parkinson’s disease, leading to an accuracy of 95.31\%. The reduced preprocessing needed for obtaining this high performance, in addition to the information provided about the reliability of the predictions evidence the applicability of the system to be used as an aid for clinicians.},
	urldate = {2024-08-14},
	journal = {Information Fusion},
	author = {Arco, Juan E. and Ortiz, Andrés and Ramírez, Javier and Martínez-Murcia, Francisco J. and Zhang, Yu-Dong and Górriz, Juan M.},
	month = jan,
	year = {2023},
	keywords = {Bayesian Deep Learning, Ensemble classification, Parkinson’s, Pneumonia, Uncertainty},
	pages = {53--65},
}

@inproceedings{stanley_flexible_2023,
	address = {Cham},
	title = {A {Flexible} {Framework} for {Simulating} and {Evaluating} {Biases} in {Deep} {Learning}-{Based} {Medical} {Image} {Analysis}},
	isbn = {978-3-031-43895-0},
	doi = {10.1007/978-3-031-43895-0_46},
	abstract = {Despite the remarkable advances in deep learning for medical image analysis, it has become evident that biases in datasets used for training such models pose considerable challenges for a clinical deployment, including fairness and domain generalization issues. Although the development of bias mitigation techniques has become ubiquitous, the nature of inherent and unknown biases in real-world medical image data prevents a comprehensive understanding of algorithmic bias when developing deep learning models and bias mitigation methods. To address this challenge, we propose a modular and customizable framework for bias simulation in synthetic but realistic medical imaging data. Our framework provides complete control and flexibility for simulating a range of bias scenarios that can lead to undesired model performance and shortcut learning. In this work, we demonstrate how this framework can be used to simulate morphological biases in neuroimaging data for disease classification with a convolutional neural network as a first feasibility analysis. Using this case example, we show how the proportion of bias in the disease class and proximity between disease and bias regions can affect model performance and explainability results. The proposed framework provides the opportunity to objectively and comprehensively study how biases in medical image data affect deep learning pipelines, which will facilitate a better understanding of how to responsibly develop models and bias mitigation methods for clinical use. Code is available at github.com/estanley16/SimBA.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2023},
	publisher = {Springer Nature Switzerland},
	author = {Stanley, Emma A. M. and Wilms, Matthias and Forkert, Nils D.},
	editor = {Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood, Tanveer and Taylor, Russell},
	year = {2023},
	pages = {489--499},
}

@inproceedings{zhang_wall_2023,
	address = {Cham},
	title = {Wall {Thickness} {Estimation} from {Short} {Axis} {Ultrasound} {Images} via {Temporal} {Compatible} {Deformation} {Learning}},
	isbn = {978-3-031-43987-2},
	doi = {10.1007/978-3-031-43987-2_21},
	abstract = {Structural parameters of the heart, such as left ventricular wall thickness (LVWT), have important clinical significance for cardiac disease. In clinical practice, it requires tedious labor work to be obtained manually from ultrasound images and results in large variations between experts. Great challenges exist to automatize this procedure: the myocardium boundary is sensitive to heavy noise and can lead to irregular boundaries; the temporal dynamics in the ultrasound video are not well retained. In this paper, we propose a Temporally Compatible Deformation learning network, named TC-Deformer, to detect the myocardium boundaries and estimate LVWT automatically. Specifically, we first propose a two-stage deformation learning network to estimate the myocardium boundaries by deforming a prior myocardium template. A global affine transformation is first learned to shift and scale the template. Then a dense deformation field is learned to adjust locally the template to match the myocardium boundaries. Second, to make the deformation learning of different frames become compatible in the temporal dynamics, we adopt the mean parameters of affine transformation for all frames and propose a bi-direction deformation learning to guarantee that the deformation fields across the whole sequences can be applied to both the myocardium boundaries and the ultrasound images. Experimental results on an ultrasound dataset of 201 participants show that the proposed method can achieve good boundary detection of basal, middle, and apical myocardium, and lead to accurate estimation of the LVWT, with a mean absolute error of less than 1.00 mm. When compared with human methods, our TC-Deformer performs better than the junior cardiologists and is on par with the middle-level cardiologists.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2023},
	publisher = {Springer Nature Switzerland},
	author = {Zhang, Ang and Peng, Guijuan and Zheng, Jialan and Cheng, Jun and Liu, Xiaohua and Liu, Qian and Sheng, Yuanyuan and Zheng, Yingqi and Yang, Yumei and Deng, Jie and Liu, Yingying and Xue, Wufeng and Ni, Dong},
	editor = {Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood, Tanveer and Taylor, Russell},
	year = {2023},
	keywords = {Deformation learning, Segmentation, Wall thickness},
	pages = {213--222},
}

@inproceedings{lin_pmc-clip_2023,
	address = {Cham},
	title = {{PMC}-{CLIP}: {Contrastive} {Language}-{Image} {Pre}-training {Using} {Biomedical} {Documents}},
	isbn = {978-3-031-43993-3},
	shorttitle = {{PMC}-{CLIP}},
	doi = {10.1007/978-3-031-43993-3_51},
	abstract = {Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In contrast, development in biomedical domain lags far behind due to data scarcity. To address this issue, we build and release PMC-OA, a biomedical dataset with 1.6M image-caption pairs collected from PubMedCentral’s OpenAccess subset, which is 8 times larger than before, PMC-OA covers diverse modalities or diseases, with majority of the image-caption samples aligned at finer-grained level, i.e., subfigure and subcaption. While pretraining a CLIP-style model on PMC-OA, our model named PMC-CLIP outperform previous state-of-the-art models on various downstream tasks, including image-text retrieval on ROCO, MedMNIST image classification, Medical VQA, for example, +8.1\% R@10 on image-text retrieval, +3.9\% accuracy on image classification.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2023},
	publisher = {Springer Nature Switzerland},
	author = {Lin, Weixiong and Zhao, Ziheng and Zhang, Xiaoman and Wu, Chaoyi and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
	editor = {Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood, Tanveer and Taylor, Russell},
	year = {2023},
	keywords = {Multimodal Dataset, Vision-Language Pretraining},
	pages = {525--536},
}

@inproceedings{smith_relevance_2023,
	address = {Cham},
	title = {On the {Relevance} of {Temporal} {Features} for {Medical} {Ultrasound} {Video} {Recognition}},
	isbn = {978-3-031-43895-0},
	doi = {10.1007/978-3-031-43895-0_70},
	abstract = {Many medical ultrasound video recognition tasks involve identifying key anatomical features regardless of when they appear in the video suggesting that modeling such tasks may not benefit from temporal features. Correspondingly, model architectures that exclude temporal features may have better sample efficiency. We propose a novel multi-head attention architecture that incorporates these hypotheses as inductive priors to achieve better sample efficiency on common ultrasound tasks. We compare the performance of our architecture to an efficient 3D CNN video recognition model in two settings: one where we expect not to require temporal features and one where we do. In the former setting, our model outperforms the 3D CNN - especially when we artificially limit the training data. In the latter, the outcome reverses. These results suggest that expressive time-independent models may be more effective than state-of-the-art video recognition models for some common ultrasound tasks in the low-data regime. Code is available at https://github.com/MedAI-Clemson/pda\_detection.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2023},
	publisher = {Springer Nature Switzerland},
	author = {Smith, D. Hudson and Lineberger, John Paul and Baker, George H.},
	editor = {Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood, Tanveer and Taylor, Russell},
	year = {2023},
	keywords = {Attention, Sample Efficiency, Ultrasound, Video},
	pages = {744--753},
}

@inproceedings{mishra_dual_2023,
	address = {Cham},
	title = {Dual {Conditioned} {Diffusion} {Models} for {Out}-of-{Distribution} {Detection}: {Application} to {Fetal} {Ultrasound} {Videos}},
	isbn = {978-3-031-43907-0},
	shorttitle = {Dual {Conditioned} {Diffusion} {Models} for {Out}-of-{Distribution} {Detection}},
	doi = {10.1007/978-3-031-43907-0_21},
	abstract = {Out-of-distribution (OOD) detection is essential to improve the reliability of machine learning models by detecting samples that do not belong to the training distribution. Detecting OOD samples effectively in certain tasks can pose a challenge because of the substantial heterogeneity within the in-distribution (ID), and the high structural similarity between ID and OOD classes. For instance, when detecting heart views in fetal ultrasound videos there is a high structural similarity between the heart and other anatomies such as the abdomen, and large in-distribution variance as a heart has 5 distinct views and structural variations within each view. To detect OOD samples in this context, the resulting model should generalise to the intra-anatomy variations while rejecting similar OOD samples. In this paper, we introduce dual-conditioned diffusion models (DCDM) where we condition the model on in-distribution class information and latent features of the input image for reconstruction-based OOD detection. This constrains the generative manifold of the model to generate images structurally and semantically similar to those within the in-distribution. The proposed model outperforms reference methods with a 12\% improvement in accuracy, 22\% higher precision, and an 8\% better F1 score.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2023},
	publisher = {Springer Nature Switzerland},
	author = {Mishra, Divyanshu and Zhao, He and Saha, Pramit and Papageorghiou, Aris T. and Noble, J. Alison},
	editor = {Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood, Tanveer and Taylor, Russell},
	year = {2023},
	pages = {216--226},
}

@inproceedings{huang_style_2023,
	address = {Cham},
	title = {A {Style} {Transfer}-{Based} {Augmentation} {Framework} for {Improving} {Segmentation} and {Classification} {Performance} {Across} {Different} {Sources} in {Ultrasound} {Images}},
	isbn = {978-3-031-43987-2},
	doi = {10.1007/978-3-031-43987-2_5},
	abstract = {Ultrasound imaging can vary in style/appearance due to differences in scanning equipment and other factors, resulting in degraded segmentation and classification performance of deep learning models for ultrasound image analysis. Previous studies have attempted to solve this problem by using style transfer and augmentation techniques, but these methods usually require a large amount of data from multiple sources and source-specific discriminators, which are not feasible for medical datasets with limited samples. Moreover, finding suitable augmentation methods for ultrasound data can be difficult. To address these challenges, we propose a novel style transfer-based augmentation framework that consists of three components: mixed style augmentation (MixStyleAug), feature augmentation (FeatAug), and mask-based style augmentation (MaskAug). MixStyleAug uses a style transfer network to transform the style of a training image into various reference styles, which enriches the information from different sources for the network. FeatAug augments the styles at the feature level to compensate for possible style variations, especially for small-size datasets with limited styles. MaskAug leverages segmentation masks to highlight the key regions in the images, which enhances the model’s generalizability. We evaluate our framework on five ultrasound datasets collected from different scanners and centers. Our framework outperforms previous methods on both segmentation and classification tasks, especially on small-size datasets. Our results suggest that our framework can effectively improve the performance of deep learning models across different ultrasound sources with limited data.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2023},
	publisher = {Springer Nature Switzerland},
	author = {Huang, Bin and Xu, Ziyue and Chan, Shing-Chow and Liu, Zhong and Wen, Huiying and Hou, Chao and Huang, Qicai and Jiang, Meiqin and Dong, Changfeng and Zeng, Jie and Zou, Ruhai and Huang, Bingsheng and Chen, Xin and Li, Shuo},
	editor = {Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood, Tanveer and Taylor, Russell},
	year = {2023},
	keywords = {Classification, Data augmentation, Segmentation, Style transfer, Ultrasound},
	pages = {44--53},
}

@inproceedings{wei_iclip_2023,
	title = {{iCLIP}: {Bridging} {Image} {Classification} and {Contrastive} {Language}-{Image} {Pre}-training for {Visual} {Recognition}},
	shorttitle = {{iCLIP}},
	url = {https://ieeexplore.ieee.org/document/10203973/?arnumber=10203973},
	doi = {10.1109/CVPR52729.2023.00272},
	abstract = {This paper presents a method that effectively combines two prevalent visual recognition methods, i.e., image classification and contrastive language-image pre-training, dubbed iCLIP. Instead of naïve multi-task learning that use two separate heads for each task, we fuse the two tasks in a deep fashion that adapts the image classification to share the same formula and the same model weights with the language-image pre-training. To further bridge these two tasks, we propose to enhance the category names in image classification tasks using external knowledge, such as their descriptions in dictionaries. Extensive experiments show that the proposed method combines the advantages of two tasks well: the strong discrimination ability in image classification tasks due to the clean category labels, and the good zero-shot ability in CLIP tasks ascribed to the richer semantics in the text descriptions. In particular, it reaches 82.9\% top-1 accuracy on IN-1K, and mean-while surpasses CLIP by 1.8\%, with similar model size, on zero-shot recognition of Kornblith 12-dataset benchmark. The code and models are publicly available at https://github.com/weiyx16/iCLIP.},
	urldate = {2024-07-18},
	booktitle = {2023 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	author = {Wei, Yixuan and Cao, Yue and Zhang, Zheng and Peng, Houwen and Yao, Zhuliang and Xie, Zhenda and Hu, Han and Guo, Baining},
	month = jun,
	year = {2023},
	note = {ISSN: 2575-7075},
	keywords = {Bridges, Head, Image recognition, Multitasking, Representation learning, Semantics, Vision, Visualization, language, reasoning},
	pages = {2776--2786},
}

@inproceedings{nauta_pip-net_2023,
	title = {{PIP}-{Net}: {Patch}-{Based} {Intuitive} {Prototypes} for {Interpretable} {Image} {Classification}},
	shorttitle = {{PIP}-{Net}},
	url = {https://ieeexplore.ieee.org/document/10204807},
	doi = {10.1109/CVPR52729.2023.00269},
	abstract = {Interpretable methods based on prototypical patches recognize various components in an image in order to explain their reasoning to humans. However, existing prototype-based methods can learn prototypes that are not in line with human visual perception, i.e., the same prototype can refer to different concepts in the real world, making interpretation not intuitive. Driven by the principle of explainability-by-design, we introduce PIP-Net (Patch-based Intuitive Prototypes Network): an interpretable image classification model that learns prototypical parts in a self-supervised fashion which correlate better with human vision. PIP-Net can be interpreted as a sparse scoring sheet where the presence of a prototypical part in an image adds evidence for a class. The model can also abstain from a decision for out-of-distribution data by saying “I haven't seen this before”. We only use image-level labels and do not rely on any part annotations. PIP-Net is globally interpretable since the set of learned prototypes shows the entire reasoning of the model. A smaller local explanation locates the relevant prototypes in one image. We show that our prototypes correlate with ground-truth object parts, indicating that PIP-Net closes the “semantic gap” between latent space and pixel space. Hence, our PIP-Net with interpretable prototypes enables users to interpret the decision making process in an intuitive, faithful and semantically meaningful way. Code is available at https://github.com/M-Nauta/PIPNet.},
	urldate = {2024-07-18},
	booktitle = {2023 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	author = {Nauta, Meike and Schlötterer, Jörg and van Keulen, Maurice and Seifert, Christin},
	month = jun,
	year = {2023},
	note = {ISSN: 2575-7075},
	keywords = {Annotations, Cognition, Decision making, Explainable computer vision, Manuals, Pattern recognition, Prototypes, Semantics},
	pages = {2744--2753},
}

@misc{yang_directional_2023,
	title = {Directional {Connectivity}-based {Segmentation} of {Medical} {Images}},
	url = {https://arxiv.org/abs/2304.00145v1},
	abstract = {Anatomical consistency in biomarker segmentation is crucial for many medical image analysis tasks. A promising paradigm for achieving anatomically consistent segmentation via deep networks is incorporating pixel connectivity, a basic concept in digital topology, to model inter-pixel relationships. However, previous works on connectivity modeling have ignored the rich channel-wise directional information in the latent space. In this work, we demonstrate that effective disentanglement of directional sub-space from the shared latent space can significantly enhance the feature representation in the connectivity-based network. To this end, we propose a directional connectivity modeling scheme for segmentation that decouples, tracks, and utilizes the directional information across the network. Experiments on various public medical image segmentation benchmarks show the effectiveness of our model as compared to the state-of-the-art methods. Code is available at https://github.com/Zyun-Y/DconnNet.},
	language = {en},
	urldate = {2024-07-18},
	journal = {arXiv.org},
	author = {Yang, Ziyun and Farsiu, Sina},
	month = mar,
	year = {2023},
}

@misc{bai_bidirectional_2023,
	title = {Bidirectional {Copy}-{Paste} for {Semi}-{Supervised} {Medical} {Image} {Segmentation}},
	url = {http://arxiv.org/abs/2305.00673},
	doi = {10.48550/arXiv.2305.00673},
	abstract = {In semi-supervised medical image segmentation, there exist empirical mismatch problems between labeled and unlabeled data distribution. The knowledge learned from the labeled data may be largely discarded if treating labeled and unlabeled data separately or in an inconsistent manner. We propose a straightforward method for alleviating the problem - copy-pasting labeled and unlabeled data bidirectionally, in a simple Mean Teacher architecture. The method encourages unlabeled data to learn comprehensive common semantics from the labeled data in both inward and outward directions. More importantly, the consistent learning procedure for labeled and unlabeled data can largely reduce the empirical distribution gap. In detail, we copy-paste a random crop from a labeled image (foreground) onto an unlabeled image (background) and an unlabeled image (foreground) onto a labeled image (background), respectively. The two mixed images are fed into a Student network and supervised by the mixed supervisory signals of pseudo-labels and ground-truth. We reveal that the simple mechanism of copy-pasting bidirectionally between labeled and unlabeled data is good enough and the experiments show solid gains (e.g., over 21\% Dice improvement on ACDC dataset with 5\% labeled data) compared with other state-of-the-arts on various semi-supervised medical image segmentation datasets. Code is available at https://github.com/DeepMed-Lab-ECNU/BCP\vphantom{\{}\}.},
	urldate = {2024-07-18},
	publisher = {arXiv},
	author = {Bai, Yunhao and Chen, Duowen and Li, Qingli and Shen, Wei and Wang, Yan},
	month = may,
	year = {2023},
	note = {arXiv:2305.00673 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@inproceedings{zeng_pefat_2023,
	title = {{PEFAT}: {Boosting} {Semi}-{Supervised} {Medical} {Image} {Classification} via {Pseudo}-{Loss} {Estimation} and {Feature} {Adversarial} {Training}},
	shorttitle = {{PEFAT}},
	url = {https://ieeexplore.ieee.org/document/10203490},
	doi = {10.1109/CVPR52729.2023.01504},
	abstract = {Pseudo-labeling approaches have been proven beneficial for semi-supervised learning (SSL) schemes in computer vision and medical imaging. Most works are dedicated to finding samples with high-confidence pseudo-labels from the perspective of model predicted probability. Whereas this way may lead to the inclusion of incorrectly pseudo-labeled data if the threshold is not carefully adjusted. In addition, low-confidence probability samples are frequently disregarded and not employed to their full potential. In this paper, we propose a novel Pseudo-loss Estimation and Feature Adversarial Training semi-supervised framework, termed as PEFAT, to boost the performance of multi-class and multi-label medical image classification from the point of loss distribution modeling and adversarial training. Specifically, we develop a trustworthy data selection scheme to split a high-quality pseudo-labeled set, inspired by the dividable pseudo-loss assumption that clean data tend to show lower loss while noise data is the opposite. Instead of directly discarding these samples with low-quality pseudo-labels, we present a novel regularization approach to learn discriminate information from them via injecting adversarial noises at the feature-level to smooth the decision boundary. Experimental results on three medical and two natural image benchmarks validate that our PEFAT can achieve a promising performance and surpass other state-of-the-art methods. The code is available at https://github.com/maxwell0027/PEFAT.},
	urldate = {2024-07-18},
	booktitle = {2023 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	author = {Zeng, Qingjie and Xie, Yutong and Lu, Zilin and Xia, Yong},
	month = jun,
	year = {2023},
	note = {ISSN: 2575-7075},
	keywords = {Computer vision, Data models, Estimation, Medical and biological vision, Pattern recognition, Predictive models, Semisupervised learning, Training, cell microscopy},
	pages = {15671--15680},
}

@misc{noauthor_pefat_nodate,
	title = {{PEFAT}: {Boosting} {Semi}-{Supervised} {Medical} {Image} {Classification} via {Pseudo}-{Loss} {Estimation} and {Feature} {Adversarial} {Training} {\textbar} {IEEE} {Conference} {Publication} {\textbar} {IEEE} {Xplore}},
	url = {https://ieeexplore.ieee.org/document/10203490},
	urldate = {2024-07-18},
}

@misc{langer_deeptechnome_2022,
	title = {{DeepTechnome}: {Mitigating} {Unknown} {Bias} in {Deep} {Learning} {Based} {Assessment} of {CT} {Images}},
	shorttitle = {{DeepTechnome}},
	url = {http://arxiv.org/abs/2205.13297},
	doi = {10.48550/arXiv.2205.13297},
	abstract = {Reliably detecting diseases using relevant biological information is crucial for real-world applicability of deep learning techniques in medical imaging. We debias deep learning models during training against unknown bias - without preprocessing/filtering the input beforehand or assuming specific knowledge about its distribution or precise nature in the dataset. We use control regions as surrogates that carry information regarding the bias, employ the classifier model to extract features, and suppress biased intermediate features with our custom, modular DecorreLayer. We evaluate our method on a dataset of 952 lung computed tomography scans by introducing simulated biases w.r.t. reconstruction kernel and noise level and propose including an adversarial test set in evaluations of bias reduction techniques. In a moderately sized model architecture, applying the proposed method to learn from data exhibiting a strong bias, it near-perfectly recovers the classification performance observed when training with corresponding unbiased data.},
	urldate = {2024-07-18},
	publisher = {arXiv},
	author = {Langer, Simon and Taubmann, Oliver and Denzinger, Felix and Maier, Andreas and Mühlberg, Alexander},
	month = may,
	year = {2022},
	note = {arXiv:2205.13297 [cs, eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, I.2.6, I.4, I.5},
}

@article{kang_accurate_2021,
	title = {Accurate {Landmark} {Localization} for {Medical} {Images} {Using} {Perturbations}},
	volume = {11},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2076-3417},
	url = {https://www.mdpi.com/2076-3417/11/21/10277},
	doi = {10.3390/app112110277},
	abstract = {Recently, various studies have been proposed to learn the rich representations of images during deep learning. In particular, the perturbation method is a simple way to learn rich representations that has shown significant success. In this study, we present effective perturbation approaches for medical landmark localization. To this end, we report an extensive experiment that uses the perturbation methods of erasing, smoothing, binarization, and edge detection. The hand X-ray dataset and the ISBI 2015 Cephalometric dataset are used to evaluate the perturbation effect. The experimental results show that the perturbation method forces the network to extract richer representations of an image, leading to performance increases. Moreover, in comparison with the existing methods that lack any complex algorithmic change of network, our methods with specific perturbation methods achieve superior performance.},
	language = {en},
	number = {21},
	urldate = {2024-05-28},
	journal = {Applied Sciences},
	author = {Kang, Junhyeok and Oh, Kanghan and Oh, Il-Seok},
	month = jan,
	year = {2021},
	note = {Number: 21
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {artificial intelligence, context feature learning, image perturbation, landmark localization},
	pages = {10277},
}

@article{bhatkalkar_fundusposnet_2021,
	title = {{FundusPosNet}: {A} {Deep} {Learning} {Driven} {Heatmap} {Regression} {Model} for the {Joint} {Localization} of {Optic} {Disc} and {Fovea} {Centers} in {Color} {Fundus} {Images}},
	volume = {9},
	issn = {2169-3536},
	shorttitle = {{FundusPosNet}},
	url = {https://ieeexplore.ieee.org/document/9611261},
	doi = {10.1109/ACCESS.2021.3127280},
	abstract = {The localization of the optic disc and fovea is crucial in the automated diagnosis of various retinal diseases. We propose a novel deep learning driven heatmap regression model based on the encoder-decoder architecture for the joint detection of optic disc and fovea centers in color fundus images. To train the regression model, we transform the ground-truth center coordinates of optic disc and fovea of the IDRiD dataset to heatmaps using a 2D-Gaussian equation. The model is capable of pinpointing any single pixel in a vast 2D image space. The model is tested on IDRiD test dataset, Messidor, and G1020 datasets. The model outperforms the state-of-the-art methods on these datasets. The model is very robust and generic, which can be trained and used for the simultaneous localization of multiple landmarks in different medical image datasets. The full implementation code and the trained model with weights (based on Keras) are available for reuse at https://github.com/bhargav-jb/FundusPosNet.},
	urldate = {2024-05-28},
	journal = {IEEE Access},
	author = {Bhatkalkar, Bhargav J. and Nayak, S. Vighnesh and Shenoy, Sathvik V. and Arjunan, R. Vijaya},
	year = {2021},
	note = {Conference Name: IEEE Access},
	keywords = {Adaptive optics, Biomedical optical imaging, Fundus image, Gaussian blob, Heating systems, Mathematical models, Nonlinear optics, Optical imaging, Retina, deep learning, fovea, heatmap, optic disc, regression neural network},
	pages = {159071--159080},
}

@article{bavirisetti_multi-task_2023,
	title = {A {Multi}-{Task} {Vision} {Transformer} for {Segmentation} and {Monocular} {Depth} {Estimation} for {Autonomous} {Vehicles}},
	volume = {4},
	issn = {2687-7813},
	url = {https://ieeexplore.ieee.org/abstract/document/10330677},
	doi = {10.1109/OJITS.2023.3335648},
	abstract = {In this paper, we investigate the use of Vision Transformers for processing and understanding visual data in an autonomous driving setting. Specifically, we explore the use of Vision Transformers for semantic segmentation and monocular depth estimation using only a single image as input. We present state-of-the-art Vision Transformers for these tasks and combine them into a multitask model. Through multiple experiments on four different street image datasets, we demonstrate that the multitask approach significantly reduces inference time while maintaining high accuracy for both tasks. Additionally, we show that changing the size of the Transformer-based backbone can be used as a trade-off between inference speed and accuracy. Furthermore, we investigate the use of synthetic data for pre-training and show that it effectively increases the accuracy of the model when real-world data is limited.},
	urldate = {2024-05-28},
	journal = {IEEE Open Journal of Intelligent Transportation Systems},
	author = {Bavirisetti, Durga Prasad and Martinsen, Herman Ryen and Kiss, Gabriel Hanssen and Lindseth, Frank},
	year = {2023},
	note = {Conference Name: IEEE Open Journal of Intelligent Transportation Systems},
	keywords = {Autonomous vehicles, Computer architecture, Estimation, Semantic segmentation, Semantics, Task analysis, Transformers, Vision transformer, autonomous vehicles, monocular depth prediction, multi-task, segmentation},
	pages = {909--928},
}

@misc{motorcu_hm-net_2021,
	title = {{HM}-{Net}: {A} {Regression} {Network} for {Object} {Center} {Detection} and {Tracking} on {Wide} {Area} {Motion} {Imagery}},
	shorttitle = {{HM}-{Net}},
	url = {http://arxiv.org/abs/2110.09881},
	abstract = {Wide Area Motion Imagery (WAMI) yields high resolution images with a large number of extremely small objects. Target objects have large spatial displacements throughout consecutive frames. This nature of WAMI images makes object tracking and detection challenging. In this paper, we present our deep neural network-based combined object detection and tracking model, namely, Heat Map Network (HM-Net). HM-Net is signiﬁcantly faster than state-of-the-art frame differencing and background subtraction-based methods, without compromising detection and tracking performances. HM-Net follows object center-based joint detection and tracking paradigm. Simple heat map-based predictions support unlimited number of simultaneous detections. The proposed method uses two consecutive frames and the object detection heat map obtained from the previous frame as input, which helps HM-Net monitor spatiotemporal changes between frames and keep track of previously predicted objects. Although reuse of prior object detection heat map acts as a vital feedback-based memory element, it can lead to unintended surge of false positive detections. To increase robustness of the method against false positives and to eliminate low conﬁdence detections, HM-Net employs novel feedback ﬁlters and advanced data augmentations. HM-Net outperforms stateof-the-art WAMI moving object detection and tracking methods on WPAFB dataset with its 96.2\% F1 and 94.4\% mAP detection scores, while achieving 61.8\% mAP tracking score on the same dataset. This performance corresponds to an improvement of 2.1\% for F1, 6.1\% for mAP scores on detection, and 9.5\% for mAP score on tracking over state-of-the-art.},
	language = {en},
	urldate = {2024-05-27},
	publisher = {arXiv},
	author = {Motorcu, Hakki and Ates, Hasan F. and Ugurdag, H. Fatih and Gunturk, Bahadir},
	month = dec,
	year = {2021},
	note = {arXiv:2110.09881 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{noauthor_ieee_nodate,
	title = {{IEEE} {Xplore} {Full}-{Text} {PDF}:},
	url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9611261},
	urldate = {2024-05-27},
}

@article{duong_fusion_2023,
	title = {Fusion of edge detection and graph neural networks to classifying electrocardiogram signals},
	volume = {225},
	issn = {0957-4174},
	url = {https://www.sciencedirect.com/science/article/pii/S0957417423006097},
	doi = {10.1016/j.eswa.2023.120107},
	abstract = {The analysis of electrocardiogram (ECG) signals are among the key factors in the diagnosis of cardiovascular diseases (CVDs). However, automatic processing of ECG in clinical practice is still restrained by the accuracy of existing algorithms. Deep learning methods have recently achieved striking success in a variety of task including predictive healthcare. Graph neural networks are a class of machine learning algorithms which can learn by directly extracting important information from graph-structured data, and perform prediction on unknown data. Such algorithms are suitable for mining complex graph data, deducing useful predictions. In this work, we present a Graph Neural Network (GNN) model trained in two datasets with more than 107,000 single-lead signal images extracted from laboratories of Boston’s Beth Israel Hospital and of the Massachusetts Institute of Technology (MITBIH), and 1.5 million labeled exams analyzed by the Physikalisch-Technische Bundesanstalt (PTB). Our proposed GNN achieves promising performance, i.e., the results show that ECG classification based on GNNs using either single-lead or 12-lead setup is closer to the human-level in standard clinical practice. By several testing instances, the proposed approach obtains an accuracy of 1.0, thereby outperforming various state-of-the-art baselines by both databases with respect to effectiveness and timing efficiency. We anticipate that the approach can be deployed as a non-invasive pre-screening tool to assist doctors in real-time monitoring and performing their diagnosis activities.},
	urldate = {2024-05-14},
	journal = {Expert Systems with Applications},
	author = {Duong, Linh T. and Doan, Thu T. H. and Chu, Cong Q. and Nguyen, Phuong T.},
	month = sep,
	year = {2023},
	keywords = {Bio-signaling, Deep learning, Electrocardiogram (ECG/EKG), Graph neural network(s), Healthcare},
	pages = {120107},
}

@misc{noauthor_anthropometric_nodate,
	title = {Anthropometric {Landmark} {Detection} in {3D} {Head} {Surfaces} {Using} a {Deep} {Learning} {Approach} {\textbar} {IEEE} {Journals} \& {Magazine} {\textbar} {IEEE} {Xplore}},
	url = {https://ieeexplore.ieee.org/abstract/document/9248587?casa_token=BawWnaJ5mvIAAAAA:SiN5_5Pfi_L46ySjv0x3mHpEcUvdd0SRxdJo-EPFbTDF7VC4N5cl8SPzxiHZRCStq9uxnrF-OQ},
	urldate = {2024-05-11},
}

@inproceedings{li_structured_2020,
	address = {Cham},
	title = {Structured {Landmark} {Detection} via {Topology}-{Adapting} {Deep} {Graph} {Learning}},
	isbn = {978-3-030-58545-7},
	doi = {10.1007/978-3-030-58545-7_16},
	abstract = {Image landmark detection aims to automatically identify the locations of predefined fiducial points. Despite recent success in this field, higher-ordered structural modeling to capture implicit or explicit relationships among anatomical landmarks has not been adequately exploited. In this work, we present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical (e.g., hand, pelvis) landmark detection. The proposed method constructs graph signals leveraging both local image features and global shape features. The adaptive graph topology naturally explores and lands on task-specific structures which are learned end-to-end with two Graph Convolutional Networks (GCNs). Extensive experiments are conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as well as three real-world X-ray medical datasets (Cephalometric (public), Hand and Pelvis). Quantitative results comparing with the previous state-of-the-art approaches across all studied datasets indicating the superior performance in both robustness and accuracy. Qualitative visualizations of the learned graph topologies demonstrate a physically plausible connectivity laying behind the landmarks.},
	language = {en},
	booktitle = {Computer {Vision} – {ECCV} 2020},
	publisher = {Springer International Publishing},
	author = {Li, Weijian and Lu, Yuhang and Zheng, Kang and Liao, Haofu and Lin, Chihung and Luo, Jiebo and Cheng, Chi-Tung and Xiao, Jing and Lu, Le and Kuo, Chang-Fu and Miao, Shun},
	editor = {Vedaldi, Andrea and Bischof, Horst and Brox, Thomas and Frahm, Jan-Michael},
	year = {2020},
	keywords = {Adaptive topology, GCN, Landmark detection},
	pages = {266--283},
}

@article{yeh_deep_2021,
	title = {Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs},
	volume = {11},
	copyright = {2021 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-021-87141-x},
	doi = {10.1038/s41598-021-87141-x},
	abstract = {Human spinal balance assessment relies considerably on sagittal radiographic parameter measurement. Deep learning could be applied for automatic landmark detection and alignment analysis, with mild to moderate standard errors and favourable correlations with manual measurement. In this study, based on 2210 annotated images of various spinal disease aetiologies, we developed deep learning models capable of automatically locating 45 anatomic landmarks and subsequently generating 18 radiographic parameters on a whole-spine lateral radiograph. In the assessment of model performance, the localisation accuracy and learning speed were the highest for landmarks in the cervical area, followed by those in the lumbosacral, thoracic, and femoral areas. All the predicted radiographic parameters were significantly correlated with ground truth values (all p {\textless} 0.001). The human and artificial intelligence comparison revealed that the deep learning model was capable of matching the reliability of doctors for 15/18 of the parameters. The proposed automatic alignment analysis system was able to localise spinal anatomic landmarks with high accuracy and to generate various radiographic parameters with favourable correlations with manual measurements.},
	language = {en},
	number = {1},
	urldate = {2024-05-02},
	journal = {Scientific Reports},
	author = {Yeh, Yu-Cheng and Weng, Chi-Hung and Huang, Yu-Jui and Fu, Chen-Ju and Tsai, Tsung-Ting and Yeh, Chao-Yuan},
	month = apr,
	year = {2021},
	note = {Publisher: Nature Publishing Group},
	keywords = {Machine learning, Radiography, Skeleton},
	pages = {7618},
}

@article{rabjohns_pseudo-pulseless_2020,
	title = {Pseudo-pulseless electrical activity in the emergency department, an evidence based approach},
	volume = {38},
	issn = {0735-6757},
	url = {https://www.sciencedirect.com/science/article/pii/S0735675719306527},
	doi = {10.1016/j.ajem.2019.158503},
	abstract = {Introduction
A great deal of the literature has focused specifically on true pulseless electrical activity (PEA), whereas there is a dearth of research regarding pseudo-PEA. This narrative review evaluates the diagnosis and management of patients in pseudo-PEA and discusses the impact on emerging patient outcomes.
Discussion
Pseudo-PEA can be defined as evidence of cardiac activity without a detectable pulse. Distinguishing pseudo-PEA from true PEA is important for emergency physicians as the prognosis and management of these patients differ. POCUS is the tool most commonly used to diagnose pseudo-PEA and there are varying treatment strategies to manage these patients. Identifying patients in pseudo-PEA can help guide resuscitation decisions, and ultimately impact emergency response systems, patients, and families.
Conclusions
The incidence of pseudo-PEA is increasing. Effective care of these patients begins with early diagnosis of this condition and immediate treatment to warrant the greatest chance of survival. There is a need for further prospective studies surrounding pseudo-PEA as evidenced by the lack of research in the current literature.},
	number = {2},
	urldate = {2024-05-01},
	journal = {The American Journal of Emergency Medicine},
	author = {Rabjohns, Jennifer and Quan, Theodore and Boniface, Keith and Pourmand, Ali},
	month = feb,
	year = {2020},
	keywords = {Electromechanical dissociation, False-PEA, Near-PEA, PEA, Pseudo-EMD, Pseudo-PEA},
	pages = {371--375},
}

@article{tschirren_automated_2002,
	title = {Automated analysis of {Doppler} ultrasound velocity flow diagrams},
	volume = {20},
	doi = {10.1109/42.974936},
	abstract = {A highly automated method for the identification and quantization of maximum blood velocity curves from Doppler ultrasound flow diagrams is presented. The method uses an image processing scheme to analyze video-recorded image sequences of flow diagrams. The sequences are acquired, a sequence of images relating to chronological cardiac cycles is extracted, and a maximum blood velocity envelope is determined and quantified. The results are verified against hand-traced reference curves. Excellent correlation of r = 0.99 is achieved.},
	journal = {IEEE transactions on medical imaging},
	author = {Tschirren, Juerg and Lauer, Ronald and Sonka, Milan},
	month = jan,
	year = {2002},
	pages = {1422--5},
}

@article{sonmez_ultrasound_2019,
	title = {Ultrasound is useful in cardiac arrest, but we still have concerns},
	volume = {0},
	issn = {0300-9572, 1873-1570},
	url = {https://www.resuscitationjournal.com/article/S0300-9572(19)30713-0/fulltext},
	doi = {10.1016/j.resuscitation.2019.09.039},
	language = {English},
	number = {0},
	urldate = {2024-04-19},
	journal = {Resuscitation},
	author = {Sönmez, Ertan and Gülen, Bedia},
	month = nov,
	year = {2019},
	pmid = {31794785},
	note = {Publisher: Elsevier},
}

@article{kenny_doppler_2021,
	title = {The {Doppler} shock index measured by a wearable ultrasound patch accurately detects moderate-to-severe central hypovolemia during lower body negative pressure},
	volume = {2},
	issn = {2688-1152},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/emp2.12533},
	doi = {10.1002/emp2.12533},
	abstract = {Objective Moderate-to-severe hemorrhage is a life-threatening condition, which is challenging to detect in a timely fashion using traditional vital signs because of the human body's robust physiologic compensatory mechanisms. Measuring and trending blood flow could improve diagnosis of clinically significant exsanguination. A lightweight, wireless, wearable Doppler ultrasound patch that captures and trends blood flow velocity could enhance hemorrhage detection. Methods In 11 healthy volunteers undergoing simulated hemorrhage and resuscitation during graded lower body negative pressure (LBNP) and release, we studied the relationship between stroke volume (SV) and common carotid artery velocity time integral (VTI) and corrected flow time (FTc). We assessed the diagnostic accuracy of 2 variations of a novel metric, the Doppler shock index (ie, the DSIVTI and DSIFTc), at capturing moderate-to-severe central hypovolemia defined as a 30\% reduction in SV. The DSIVTI and DSIFTc are calculated as the heart rate divided by either the VTI or FTc, respectively. Results A total of 17,822 cardiac cycles were analyzed across 22 LBNP protocols. The average SV reduction to the lowest tolerated LBNP stage was 40\%; there was no clinically significant fall in the mean arterial pressure. Correlations between changing SV and the common carotid artery VTI and FTc were strong (R2 of 0.87, respectively) and concordant. The DSIVTI and DSIFTc accurately detected moderate-to-severe central hypovolemia with values for the area under the receiver operator curves of 0.96 and 0.97, respectively. Conclusion In a human model of hemorrhage and resuscitation, measures from a wearable Doppler ultrasound patch correlated strongly with SV and identified moderate-to-severe central hypovolemia with excellent diagnostic accuracy.},
	language = {en},
	number = {4},
	urldate = {2024-04-19},
	journal = {Journal of the American College of Emergency Physicians Open},
	author = {Kenny, Jon-Émile S. and Elfarnawany, Mai and Yang, Zhen and Myers, Matt and Eibl, Andrew M. and Eibl, Joseph K. and Taylor, Jenna L. and Kim, Chul Ho and Johnson, Bruce D.},
	year = {2021},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/emp2.12533},
	keywords = {carotid Doppler, corrected flow time, hemodynamic monitoring, hemorrhage, velocity time integral},
	pages = {e12533},
}

@article{rolston_time_2022,
	title = {Time is running out for manual pulse checks as ultrasound races past},
	volume = {179},
	issn = {0300-9572, 1873-1570},
	url = {https://www.resuscitationjournal.com/article/S0300-9572(22)00622-0/fulltext},
	doi = {10.1016/j.resuscitation.2022.07.031},
	language = {English},
	urldate = {2024-04-19},
	journal = {Resuscitation},
	author = {Rolston, Daniel M.},
	month = oct,
	year = {2022},
	pmid = {35908671},
	note = {Publisher: Elsevier},
	pages = {59--60},
}

@article{faldaas_hands-free_2023,
	title = {Hands-free continuous carotid {Doppler} ultrasound for detection of the pulse during cardiac arrest in a porcine model},
	volume = {15},
	issn = {2666-5204},
	url = {https://www.sciencedirect.com/science/article/pii/S2666520423000553},
	doi = {10.1016/j.resplu.2023.100412},
	abstract = {Background/Purpose
Pulse palpation is an unreliable method for diagnosing cardiac arrest. To address this limitation, continuous hemodynamic monitoring may be a viable solution. Therefore, we developed a novel, hands-free Doppler system, RescueDoppler, to detect the pulse continuously in the carotid artery.
Methods
In twelve pigs, we evaluated RescueDoppleŕs potential to measure blood flow velocity in three situations where pulse palpation of the carotid artery was insufficient: (1) systolic blood pressure below 60 mmHg, (2) ventricular fibrillation (VF) and (3) pulseless electrical activity (PEA). (1) Low blood pressure was induced using a Fogarty balloon catheter to occlude the inferior vena cava. (2) An implantable cardioverter-defibrillator induced VF. (3) Myocardial infarction after microembolization of the left coronary artery caused True-PEA. Invasive blood pressure was measured in the contralateral carotid artery. Time-averaged blood flow velocity (TAV) in the carotid artery was related to mean arterial pressure (MAP) in a linear mixed model.
Results
RescueDoppler identified pulsatile blood flow in 41/41 events with systolic blood pressure below 60 mmHg, with lowest blood pressure of 19 mmHg. In addition the absence of spontaneous circulation was identified in 21/21 VF events and true PEA in 2/2 events. The intraclass correlation coefficient within animals for TAV and MAP was 0.94 (95\% CI. 0.85–0.98).
Conclusions
In a porcine model, RescueDoppler reliably identified pulsative blood flow with blood pressures below 60 mmHg. During VF and PEA, circulatory arrest was rapidly and accurately demonstrated. RescueDoppler could potentially replace unreliable pulse palpation during cardiac arrest and cardiopulmonary resuscitation.},
	urldate = {2024-04-19},
	journal = {Resuscitation Plus},
	author = {Faldaas, Bjørn Ove and Nielsen, Erik Waage and Storm, Benjamin Stage and Lappegård, Knut Tore and How, Ole-Jakob and Nilsen, Bent Aksel and Kiss, Gabriel and Skogvoll, Eirik and Torp, Hans and Ingul, Charlotte},
	month = sep,
	year = {2023},
	keywords = {Cardiac arrest monitor, Carotid artery blood flow, Doppler ultrasound, Experimental model, PEA, Ventricular fibrillation},
	pages = {100412},
}

@article{yang_probabilistic_2005,
	title = {A probabilistic neural network as the predictive classifier of out-of-hospital defibrillation outcomes},
	volume = {64},
	issn = {0300-9572},
	url = {https://www.sciencedirect.com/science/article/pii/S0300957204002849},
	doi = {10.1016/j.resuscitation.2004.07.002},
	abstract = {Introduction:
Although modern defibrillators are nearly always successful in terminating ventricular fibrillation (VF), multiple defibrillation attempts are usually required to achieve return of spontaneous circulation (ROSC). This is potentially deleterious as cardiopulmonary resuscitation (CPR) must be discontinued during each defibrillation attempt which causes deterioration in the heart muscle and reduces the chance of ROSC from later defibrillation attempts. In this work defibrillation outcomes are predicted prior to electrical shocks using a neural network model to analyse VF time series in an attempt to avoid defibrillation attempts that do not result in ROSC.
Methods:
The 198 pre-shock VF ECG episodes from 83 cardiac arrest patients with defibrillation conversions to different outcomes were selected from the Oslo ambulance service database. A probabilistic neural network model was designed for training and testing with a cross validation method being used for the better generalisation performance.
Results:
We achieved an accuracy of 75\% in overall prediction with a sensitivity of 84\% and a specificity of 65\% using VF ECG time series of an order of 1s in length.
Conclusion:
Pre-shock VF ECG time series can be classified according to the defibrillation conversion to a return of spontaneous circulation (ROSC) or No-ROSC.},
	number = {1},
	urldate = {2024-04-19},
	journal = {Resuscitation},
	author = {Yang, Zhijun and Yang, Zhengrong and Lu, Weiping and Harrison, Robert G. and Eftestøl, Trygve and Steen, Petter A.},
	month = jan,
	year = {2005},
	keywords = {Cardiac arrest, Defibrillation, Electrocardiography, Outcome, Ventricular fibrillation},
	pages = {31--36},
}

@article{elola_capnography_2019,
	title = {Capnography: {A} support tool for the detection of return of spontaneous circulation in out-of-hospital cardiac arrest},
	volume = {142},
	issn = {0300-9572},
	shorttitle = {Capnography},
	url = {https://www.sciencedirect.com/science/article/pii/S0300957219301327},
	doi = {10.1016/j.resuscitation.2019.03.048},
	abstract = {Background
Automated detection of return of spontaneous circulation (ROSC) is still an unsolved problem during cardiac arrest. Current guidelines recommend the use of capnography, but most automatic methods are based on the analysis of the ECG and thoracic impedance (TI) signals. This study analysed the added value of EtCO2 for discriminating pulsed (PR) and pulseless (PEA) rhythms and its potential to detect ROSC.
Materials and methods
A total of 426 out-of-hospital cardiac arrest cases, 117 with ROSC and 309 without ROSC, were analysed. First, EtCO2 values were compared for ROSC and no ROSC cases. Second, 5098 artefact free 3-s long segments were automatically extracted and labelled as PR (3639) or PEA (1459) using the instant of ROSC annotated by the clinician on scene as gold standard. Machine learning classifiers were designed using features obtained from the ECG, TI and the EtCO2 value. Third, the cases were retrospectively analysed using the classifier to discriminate cases with and without ROSC.
Results
EtCO2 values increased significantly from 41 mmHg 3-min before ROSC to 57 mmHg 1-min after ROSC, and EtCO2 was significantly larger for PR than for PEA, 46 mmHg/20 mmHg (p {\textless} 0.05). Adding EtCO2 to the machine learning models increased their area under the curve (AUC) by over 2 percentage points. The combination of ECG, TI and EtCO2 had an AUC for the detection of pulse of 0.92. Finally, the retrospective analysis showed a sensitivity and specificity of 96.6\% and 94.5\% for the detection of ROSC and no-ROSC cases, respectively.
Conclusion
Adding EtCO2 improves the performance of automatic algorithms for pulse detection based on ECG and TI. These algorithms can be used to identify pulse on site, and to retrospectively identify cases with ROSC.},
	urldate = {2024-04-19},
	journal = {Resuscitation},
	author = {Elola, Andoni and Aramendi, Elisabete and Irusta, Unai and Alonso, Erik and Lu, Yuanzheng and Chang, Mary P. and Owens, Pamela and Idris, Ahamed H.},
	month = sep,
	year = {2019},
	keywords = {Capnography, Electrocardiogram (ECG), End-tidal CO (EtCO), ROSC detection, Return of spontaneous circulation (ROSC), Thoracic impedance},
	pages = {153--161},
}

@article{brinkrolf_predicting_2018,
	title = {Predicting {ROSC} in out-of-hospital cardiac arrest using expiratory carbon dioxide concentration: {Is} trend-detection instead of absolute threshold values the key?},
	volume = {122},
	issn = {0300-9572},
	shorttitle = {Predicting {ROSC} in out-of-hospital cardiac arrest using expiratory carbon dioxide concentration},
	url = {https://www.sciencedirect.com/science/article/pii/S0300957217307311},
	doi = {10.1016/j.resuscitation.2017.11.040},
	abstract = {Aim
Guidelines recommend detecting return of spontaneous circulation (ROSC) by a rising concentration of carbon dioxide in the exhalation air. As CO2 is influenced by numerous factors, no absolute cut-off values of CO2 to detect ROSC are agreed on so far. As trends in CO2 might be less affected by influencing factors, we investigated an approach which is based on detecting CO2-trends in real-time.
Methods
We conducted a retrospective case-control study on 169 CO2 time series from out of hospital cardiac arrests resuscitated by Muenster City Ambulance-Service, Germany. A recently developed statistical method for real-time trend-detection (SCARM) was applied to each time series. For each series, the percentage of time points with detected positive and negative trends was determined.
Results
ROSC time series had larger percentages of positive trends than No-ROSC time series (p=0.003). The median percentage of positive trends was 15\% in the ROSC time series (IQR: 5\% to 23\%) and 7\% in the No-ROSC time series (IQR: 3\% to 14\%). A receiver operating characteristic (ROC) analysis yielded an optimal threshold of 13\% to differentiate between ROSC and No-ROSC cases with a specificity of 58.4\% and sensitivity of 73.9\%; the area under the curve was 63.5\%.
Conclusion
Patients with ROSC differed from patients without ROSC as to the percentage of detected CO2 trends, indicating the potential of our real-time trend-detection approach. Since the study was designed as a proof of principle and its calculated specificity and sensitivity are low, more research is required to implement CO2-trend-detection into clinical use.},
	urldate = {2024-04-18},
	journal = {Resuscitation},
	author = {Brinkrolf, Peter and Borowski, Matthias and Metelmann, Camilla and Lukas, Roman-Patrik and Pidde-Küllenberg, Laura and Bohn, Andreas},
	month = jan,
	year = {2018},
	keywords = {Out of hospital cardiac arrest, Real-time, Repeated median regression, Return of spontaneous circulation (ROSC), Trend detection},
	pages = {19--24},
}

@article{jahren_estimation_2020-1,
	title = {Estimation of {End}-{Diastole} in {Cardiac} {Spectral} {Doppler} {Using} {Deep} {Learning}},
	volume = {67},
	issn = {1525-8955},
	url = {https://ieeexplore.ieee.org/abstract/document/9094189},
	doi = {10.1109/TUFFC.2020.2995118},
	abstract = {Electrocardiogram (ECG) is often used together with a spectral Doppler ultrasound to separate heart cycles by determining the end-diastole locations. However, the ECG signal is not always recorded. In such cases, the cardiac cycles can be estimated manually from the ultrasound data retrospectively. We present a deep learning-based method for automatic detection of the end-diastoles in spectral Doppler spectrograms. The method uses a combination of a convolutional neural network (CNN) for extracting features and a recurrent neural network (RNN) for modeling temporal relations. In echocardiography, there are three Doppler spectrogram modalities, continuous wave, pulsed wave, and tissue velocity Doppler. Both the training and test data sets include all three modalities. The model was tested on 643 spectrograms coming from different hospitals than in the training data set. For the purposes described in this work, a valid end-diastole detection is defined as a prediction being closer than 60 ms to the reference value. We will refer to these as true detections. Similarly, a prediction farther away is defined as nonvalid or false detections. The method automatically rejects spectrograms where the detection of an end-diastole has low confidence. When setting the algorithm to reject 1.9\%, the method achieved 97.7\% true detections with a mean error of 14 ms and had 2.5\% false detections on the remaining spectrograms.},
	number = {12},
	urldate = {2024-04-18},
	journal = {IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control},
	author = {Jahren, Tollef Struksnes and Steen, Erik N. and Aase, Svein Arne and Solberg, Anne H. Schistad},
	month = dec,
	year = {2020},
	note = {Conference Name: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control},
	keywords = {Deep learning, Doppler effect, Electrocardiography, Heart, Machine learning, Spectrogram, Ultrasonic imaging, Valves, echocardiography, end-diastole, spectral Doppler},
	pages = {2605--2614},
}

@article{avila-reyes_point--care_2021,
	title = {Point-of-care ultrasound in cardiorespiratory arrest ({POCUS}-{CA}): narrative review article},
	volume = {13},
	issn = {2524-8987},
	shorttitle = {Point-of-care ultrasound in cardiorespiratory arrest ({POCUS}-{CA})},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8639882/},
	doi = {10.1186/s13089-021-00248-0},
	abstract = {The POCUS-CA (Point-of-care ultrasound in cardiac arrest) is a diagnostic tool in the Intensive Care Unit and Emergency Department setting. The literature indicates that in the patient in a cardiorespiratory arrest it can provide information of the etiology of the arrest in patients with non-defibrillable rhythms, assess the quality of compressions during cardiopulmonary resuscitation (CPR), and define prognosis of survival according to specific findings and, thus, assist the clinician in decision-making during resuscitation. This narrative review of the literature aims to expose the usefulness of ultrasound in the setting of cardiorespiratory arrest as a tool that allows making a rapid diagnosis and making decisions about reversible causes of this entity. More studies are needed to support the evidence to make ultrasound part of the resuscitation algorithms. Teamwork during cardiopulmonary resuscitation and the inclusion of ultrasound in a multidisciplinary approach is important to achieve a favorable clinical outcome.},
	urldate = {2024-04-18},
	journal = {The Ultrasound Journal},
	author = {Ávila-Reyes, Diana and Acevedo-Cardona, Andrés O. and Gómez-González, José F. and Echeverry-Piedrahita, David R. and Aguirre-Flórez, Mateo and Giraldo-Diaconeasa, Adrian},
	month = dec,
	year = {2021},
	pmid = {34855015},
	pmcid = {PMC8639882},
	pages = {46},
}

@article{gaspari_femoral_2023-1,
	title = {Femoral {Arterial} {Doppler} {Use} {During} {Active} {Cardiopulmonary} {Resuscitation}},
	volume = {81},
	issn = {0196-0644},
	url = {https://www.sciencedirect.com/science/article/pii/S0196064422013269},
	doi = {10.1016/j.annemergmed.2022.12.002},
	abstract = {Study Objective
This study explored femoral arterial Doppler during active cardiopulmonary resuscitation (CPR) to identify and characterize the resumptions of cardiac activity without stopping CPR.
Methods
This was a proof-of-concept study exploring arterial Doppler during cardiac arrest. Patients in cardiac arrest undergoing active CPR were prospectively enrolled. Arterial Doppler of the common femoral artery was recorded during CPR and during pauses in CPR. CPR-induced arterial tracings and native cardiac-induced tracings were analyzed for rate and peak systolic velocity. Cardiac activity on echocardiogram during pause in CPR was classified as “absent,” “disorganized,” or “organized.” Descriptive data and survival are presented as mean and 95\% confidence intervals (CI), as well as sensitivity and specificity of Doppler during active CPR in detecting native cardiac pulsations.
Results
Sixteen patients with 48 paired Doppler recordings during active CPR, pause in CPR, and associated echocardiogram were enrolled. Native cardiac-induced tracings were visible during 39.6\% of pauses in CPR (19 of 48) and during 18.8\% of the periods of active CPR (9 of 48). Arterial pulsations were more frequently visualized with organized contractions by echocardiogram (10 of 14, 71\%) than disorganized contractions (9 of 22, 41\%). Arterial Doppler was 100\% specific and 50\% sensitive in detecting organized cardiac activity during active CPR. Patients with visible native cardiac pulsations during active CPR demonstrated 0\% mortality compared with 67\% mortality without visible arterial pulsations.
Conclusion
Arterial Doppler tracings may identify the resumption of native cardiac activity during active CPR; however, more research is needed.},
	number = {5},
	urldate = {2024-04-18},
	journal = {Annals of Emergency Medicine},
	author = {Gaspari, Romolo J. and Lindsay, Robert and Dowd, Andrew and Gleeson, Timothy},
	month = may,
	year = {2023},
	pages = {523--531},
}

@article{hessulf_predicting_2023,
	title = {Predicting survival and neurological outcome in out-of-hospital cardiac arrest using machine learning: the {SCARS} model},
	volume = {89},
	issn = {2352-3964},
	shorttitle = {Predicting survival and neurological outcome in out-of-hospital cardiac arrest using machine learning},
	url = {https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(23)00029-4/fulltext},
	doi = {10.1016/j.ebiom.2023.104464},
	language = {English},
	urldate = {2024-04-18},
	journal = {eBioMedicine},
	author = {Hessulf, Fredrik and Bhatt, Deepak L. and Engdahl, Johan and Lundgren, Peter and Omerovic, Elmir and Rawshani, Aidin and Helleryd, Edvin and Dworeck, Christian and Friberg, Hans and Redfors, Björn and Nielsen, Niklas and Myredal, Anna and Frigyesi, Attila and Herlitz, Johan and Rawshani, Araz},
	month = mar,
	year = {2023},
	pmid = {36773348},
	note = {Publisher: Elsevier},
	keywords = {Machine learning, Out-of-hospital cardiac arrest, Prediction model, Web application},
}

@article{liu_development_2022,
	title = {Development and validation of an interpretable prehospital return of spontaneous circulation ({P}-{ROSC}) score for patients with out-of-hospital cardiac arrest using machine learning: {A} retrospective study},
	volume = {48},
	issn = {2589-5370},
	shorttitle = {Development and validation of an interpretable prehospital return of spontaneous circulation ({P}-{ROSC}) score for patients with out-of-hospital cardiac arrest using machine learning},
	url = {https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(22)00152-3/fulltext},
	doi = {10.1016/j.eclinm.2022.101422},
	language = {English},
	urldate = {2024-04-18},
	journal = {eClinicalMedicine},
	author = {Liu, Nan and Liu, Mingxuan and Chen, Xinru and Ning, Yilin and Lee, Jin Wee and Siddiqui, Fahad Javaid and Saffari, Seyed Ehsan and Ho, Andrew Fu Wah and Shin, Sang Do and Ma, Matthew Huei-Ming and Tanaka, Hideharu and Ong, Marcus Eng Hock},
	month = jun,
	year = {2022},
	note = {Publisher: Elsevier},
	keywords = {Interpretable machine learning, Out-of-hospital cardiac arrest, Return of spontaneous circulation, Score},
}

@article{haddad_doppler_2023-1,
	title = {Doppler ultrasound peak systolic velocity versus end tidal carbon dioxide during pulse checks in cardiac arrest},
	volume = {183},
	issn = {0300-9572},
	url = {https://www.sciencedirect.com/science/article/pii/S0300957223000084},
	doi = {10.1016/j.resuscitation.2023.109695},
	abstract = {Background
An accurate, non-invasive measure of return of spontaneous circulation (ROSC) is needed to improve management of cardiac arrest patients.
Objectives
During a pulse check in Emergency Department (ED) cardiac arrest patients, we compared the correlation between 1) end tidal carbon dioxide (ETCO2) and systolic blood pressure (SBP), and 2) Doppler ultrasound peak systolic velocity (PSV) and SBP. Additionally, we assessed the accuracy of PSV ≥ 20 cm/sec in comparison to previously suggested ETCO2 ≥ 20 or ≥ 25 mmHg thresholds to predict ROSC with SBP ≥ 60 mmHg.
Methods
This was a secondary analysis of a previously published prospective observational study of ED cardiac arrest patients with an advanced airway and femoral arterial line in place. During each pulse check, highest SBP, highest PSV, and ETCO2 at the end of the pulse check were recorded. Spearman correlation coefficients were calculated and compared using a Fisher Z-transformation. Accuracy of previously determined PSV and ETCO2 thresholds for detecting ROSC with SBP ≥ 60 mmHg were compared using McNemar’s tests.
Results
Based on data from 35 patients with 111 pulse checks, we found a higher correlation between PSV and SBP than ETCO2 and SBP (0.71 vs 0.31; p {\textless} 0.001). Diagnostic accuracy of PSV ≥ 20 cm/sec for detecting ROSC with SBP ≥ 60 mmHg was 89\% (95\% CI: 82\%-94\%) versus 59\% (95\% CI: 49\%-68\%) and 58\% (95\% CI: 48\%-67\%) for ETCO2 ≥ 20 and ≥ 25 mmHg, respectively.
Conclusions
During a pulse check, Doppler ultrasound PSV outperformed ETCO2 for correlation with SBP and accuracy in detecting ROSC with SBP ≥ 60 mmHg.},
	urldate = {2024-04-18},
	journal = {Resuscitation},
	author = {Haddad, Ghania and Margius, Deanna and Cohen, Allison L. and Gorlin, Margaret and Jafari, Daniel and Li, Timmy and Owens, Casey and Becker, Lance and Rolston, Daniel M.},
	month = feb,
	year = {2023},
	keywords = {ACLS, CPR, Doppler, ETCO2, PSV, ROSC, Ultrasound},
	pages = {109695},
}

@article{hirano_early_2021,
	title = {Early outcome prediction for out-of-hospital cardiac arrest with initial shockable rhythm using machine learning models},
	volume = {158},
	issn = {0300-9572},
	url = {https://www.sciencedirect.com/science/article/pii/S0300957220305761},
	doi = {10.1016/j.resuscitation.2020.11.020},
	abstract = {Aim
Early outcome prediction for out-of-hospital cardiac arrest with initial shockable rhythm is useful in selecting the choice of resuscitative treatment by clinicians. This study aimed to develop and validate a machine learning-based outcome prediction model for out-of-hospital cardiac arrest with initial shockable rhythm, which can be used on patient’s arrival at the hospital.
Methods
Data were obtained from a nationwide out-of-hospital cardiac arrest registry in Japan. Of 43,350 out-of-hospital cardiac arrest patients with initial shockable rhythm registered between 2013 and 2017, patients aged {\textless}18 years and those with cardiac arrest caused by external factors were excluded. Subjects were classified into training (n = 23,668, 2013-2016 data) and test (n = 6381, data from 2017) sets for validation. Only 19 prehospital variables were used for the outcome prediction. The primary outcome was death at 1 month or survival with poor neurological function (cerebral performance category 3-5; “poor” outcome). Several machine learning models, including those based on logistic regression, support vector machine, random forest, and multilayer perceptron classifiers were compared.
Results
In validation analyses, all machine learning models performed satisfactorily with area under the receiver operating characteristic curve values of 0.882 [95\% confidence interval [CI]: 0.869-0.894] for logistic regression, 0.866 [95\% CI: 0.853-0.879] for support vector machine, 0.877 [95\% CI: 0.865-0.890] for random forest, and 0.888 [95\% CI: 0.876-0.900] for multilayer perceptron classifiers.
Conclusions
A favourable machine learning-based prognostic model available to use on patient arrival at the hospital was developed for out-of-hospital cardiac arrest with initial shockable rhythm.},
	urldate = {2024-04-18},
	journal = {Resuscitation},
	author = {Hirano, Yohei and Kondo, Yutaka and Sueyoshi, Koichiro and Okamoto, Ken and Tanaka, Hiroshi},
	month = jan,
	year = {2021},
	keywords = {Machine-learning, Neurological function, Out-of-hospital cardiac arrest, Prediction},
	pages = {49--56},
}

@article{alonso_machine_2018,
	series = {{RESUSCITATION} 2018 - {New} technologies in resuscitation: {Abstracts}},
	title = {A machine learning approach for detecting ventricular fibrillation during out-of-hospital cardiac arrest},
	volume = {130},
	issn = {0300-9572},
	url = {https://www.sciencedirect.com/science/article/pii/S0300957218304404},
	doi = {10.1016/j.resuscitation.2018.07.096},
	urldate = {2024-04-18},
	journal = {Resuscitation},
	author = {Alonso, Erik and Aramendi, Elisabete and Irusta, Unai and Daya, Mohamud},
	month = sep,
	year = {2018},
	pages = {e53--e54},
}

@article{urteaga_machine_2024,
	title = {Machine learning model to predict evolution of pulseless electrical activity during in-hospital cardiac arrest},
	volume = {17},
	issn = {2666-5204},
	url = {https://www.sciencedirect.com/science/article/pii/S2666520424000493},
	doi = {10.1016/j.resplu.2024.100598},
	abstract = {Background
During pulseless electrical activity (PEA) the cardiac mechanical and electrical functions are dissociated, a phenomenon occurring in 25–42\% of in-hospital cardiac arrest (IHCA) cases. Accurate evaluation of the likelihood of a PEA patient transitioning to return of spontaneous circulation (ROSC) may be vital for the successful resuscitation.
The aim
We sought to develop a model to automatically discriminate between PEA rhythms with favorable and unfavorable evolution to ROSC.
Methods
A dataset of 190 patients, 120 with ROSC, were acquired with defibrillators from different vendors in three hospitals. The ECG and the transthoracic impedance (TTI) signal were processed to compute 16 waveform features. Logistic regression models where designed integrating both automated features and characteristics annotated in the QRS to identify PEAs with better prognosis leading to ROSC. Cross validation techniques were applied, both patient-specific and stratified, to evaluate the performance of the algorithm.
Results
The best model consisted in a three feature algorithm that exhibited median (interquartile range) Area Under the Curve/Balanced accuracy/Sensitivity/Specificity of 80.3(9.9)/75.6(8.0)/ 77.4(15.2)/72.3(16.4) \%, respectively.
Conclusions
Information hidden in the waveforms of the ECG and TTI signals, along with QRS complex features, can predict the progression of PEA. Automated methods as the one proposed in this study, could contribute to assist in the targeted treatment of PEA in IHCA.},
	urldate = {2024-04-18},
	journal = {Resuscitation Plus},
	author = {Urteaga, Jon and Elola, Andoni and Norvik, Anders and Unneland, Eirik and Eftestøl, Trygve C. and Bhardwaj, Abhishek and Buckler, David and Abella, Benjamin S. and Skogvoll, Eirik and Aramendi, Elisabete},
	month = mar,
	year = {2024},
	keywords = {Cardiopulmonary resuscitation (CPR), Evolution prediction, Machine Learning models, Pulseless electrical activity (PEA)},
	pages = {100598},
}

@article{mansour_machine_2022,
	title = {Machine {Learning} for {Early} {Detection} of {Hypoxic}-{Ischemic} {Brain} {Injury} {After} {Cardiac} {Arrest}},
	volume = {36},
	issn = {1556-0961},
	url = {https://doi.org/10.1007/s12028-021-01405-y},
	doi = {10.1007/s12028-021-01405-y},
	abstract = {Establishing whether a patient who survived a cardiac arrest has suffered hypoxic-ischemic brain injury (HIBI) shortly after return of spontaneous circulation (ROSC) can be of paramount importance for informing families and identifying patients who may benefit the most from neuroprotective therapies. We hypothesize that using deep transfer learning on normal-appearing findings on head computed tomography (HCT) scans performed after ROSC would allow us to identify early evidence of HIBI.},
	language = {en},
	number = {3},
	urldate = {2024-04-18},
	journal = {Neurocritical Care},
	author = {Mansour, Ali and Fuhrman, Jordan D. and Ammar, Faten El and Loggini, Andrea and Davis, Jared and Lazaridis, Christos and Kramer, Christopher and Goldenberg, Fernando D. and Giger, Maryellen L.},
	month = jun,
	year = {2022},
	keywords = {Cardiac arrest, Hypoxic-ischemic, Machine learning},
	pages = {974--982},
}

@article{su_stroke_2023,
	title = {Stroke risk prediction by color {Doppler} ultrasound of carotid artery-based deep learning using {Inception} {V3} and {VGG}-16},
	volume = {14},
	issn = {1664-2295},
	url = {https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2023.1111906/full},
	doi = {10.3389/fneur.2023.1111906},
	abstract = {{\textless}sec{\textgreater}{\textless}title{\textgreater}Purpose{\textless}/title{\textgreater}{\textless}p{\textgreater}This study aims to automatically classify color Doppler images into two categories for stroke risk prediction based on the carotid plaque. The first category is high-risk carotid vulnerable plaque, and the second is stable carotid plaque.{\textless}/p{\textgreater}{\textless}/sec{\textgreater}{\textless}sec{\textgreater}{\textless}title{\textgreater}Method{\textless}/title{\textgreater}{\textless}p{\textgreater}In this research study, we used a deep learning framework based on transfer learning to classify color Doppler images into two categories: one is high-risk carotid vulnerable plaque, and the other is stable carotid plaque. The data were collected from the Second Affiliated Hospital of Fujian Medical University, including stable and vulnerable cases. A total of 87 patients with risk factors for atherosclerosis in our hospital were selected. We used 230 color Doppler ultrasound images for each category and further divided those into the training set and test set in a ratio of 70 and 30\%, respectively. We have implemented Inception V3 and VGG-16 pre-trained models for this classification task.{\textless}/p{\textgreater}{\textless}/sec{\textgreater}{\textless}sec{\textgreater}{\textless}title{\textgreater}Results{\textless}/title{\textgreater}{\textless}p{\textgreater}Using the proposed framework, we implemented two transfer deep learning models: Inception V3 and VGG-16. We achieved the highest accuracy of 93.81\% by using fine-tuned and adjusted hyperparameters according to our classification problem.{\textless}/p{\textgreater}{\textless}/sec{\textgreater}{\textless}sec{\textgreater}{\textless}title{\textgreater}Conclusion{\textless}/title{\textgreater}{\textless}p{\textgreater}In this research, we classified color Doppler ultrasound images into high-risk carotid vulnerable and stable carotid plaques. We fine-tuned pre-trained deep learning models to classify color Doppler ultrasound images according to our dataset. Our suggested framework helps prevent incorrect diagnoses caused by low image quality and individual experience, among other factors.{\textless}/p{\textgreater}{\textless}/sec{\textgreater}},
	language = {English},
	urldate = {2024-04-18},
	journal = {Frontiers in Neurology},
	author = {Su, Shan-Shan and Li, Li-Ya and Wang, Yi and Li, Yuan-Zhe},
	month = feb,
	year = {2023},
	note = {Publisher: Frontiers},
	keywords = {Color Doppler ultrasound images, Transfer Learning, carotid artery, deep learning, stroke risk prediction},
}

@article{huang_review_2023,
	title = {A review of deep learning segmentation methods for carotid artery ultrasound images},
	volume = {545},
	issn = {0925-2312},
	url = {https://www.sciencedirect.com/science/article/pii/S0925231223004216},
	doi = {10.1016/j.neucom.2023.126298},
	abstract = {The carotid artery is a critical blood vessel that supplies blood to the brain, and its health and function are essential for preventing cardiovascular diseases such as stroke. Ultrasound imaging is commonly used to diagnose the carotid artery and monitor its health, but traditional methods have limitations in terms of accuracy and efficiency. In recent years, deep learning segmentation methods have been developed to improve the diagnosis of the carotid artery, which have shown great potential for improving the accuracy and efficiency of cardiovascular diagnosis. In this paper, we aim to review and summarize the recent research on deep learning segmentation methods for the carotid artery ultrasound images. Specifically, we focus on techniques for the segmentation of the intima-media, plaque, and lumen sites, which are important for clinical diagnosis. Through our analysis of the literature, we seek to identify the key trends and challenges in this field, and to provide insights into the opportunities and challenges for future research and development in this area.},
	urldate = {2024-04-18},
	journal = {Neurocomputing},
	author = {Huang, Qinghua and Tian, Haozhe and Jia, Lizhi and Li, Ziming and Zhou, Zishu},
	month = aug,
	year = {2023},
	keywords = {Carotid artery, Computer-aided diagnosis, Deep learning, Intima-media, Lumen, Plaque, Segmentation, Ultrasound},
	pages = {126298},
}

@inproceedings{uguz_classification_2010,
	address = {Berlin, Heidelberg},
	title = {Classification of {Internal} {Carotid} {Artery} {Doppler} {Signals} {Using} {Hidden} {Markov} {Model} and {Wavelet} {Transform} with {Entropy}},
	isbn = {978-3-642-16699-0},
	doi = {10.1007/978-3-642-16699-0_20},
	abstract = {Doppler ultrasound has been usually preferred for investigation of the artery conditions in the last two decade, since it is a non-invasive method which is not risky. In this study, a biomedical system based on Discrete Hidden Markov Model (DHMM) has been developed in order to classify the internal carotid artery Doppler signals recorded from 191 subjects (136 of them had suffered from internal carotid artery stenosis and rest of them had been healthy subjects). Developed system comprises of three stages. In the first stage, for feature extraction, obtained Doppler signals were separated to its sub-bands using Discrete Wavelet Transform (DWT). In the second stage, entropy of each sub-band was calculated using Shannon entropy algorithm to reduce the dimensionality of the feature vectors via DWT. In the third stage, the reduced features of carotid artery Doppler signals were used as input patterns of the DHMM classifier. Our proposed method reached 97.38\% classification accuracy with 5 fold cross validation (CV) technique. The classification results showed that purposed method is effective for classification of internal carotid artery Doppler signals.},
	language = {en},
	booktitle = {Advances in {Information} {Technology}},
	publisher = {Springer},
	author = {Uğuz, Harun and Kodaz, Halife},
	editor = {Papasratorn, Borworn and Lavangnananda, Kittichai and Chutimaskul, Wichian and Vanijja, Vajirasak},
	year = {2010},
	pages = {183--191},
}

@article{ottakath_ultrasound-based_2023,
	title = {Ultrasound-{Based} {Image} {Analysis} for {Predicting} {Carotid} {Artery} {Stenosis} {Risk}: {A} {Comprehensive} {Review} of the {Problem}, {Techniques}, {Datasets}, and {Future} {Directions}},
	volume = {13},
	issn = {2075-4418},
	shorttitle = {Ultrasound-{Based} {Image} {Analysis} for {Predicting} {Carotid} {Artery} {Stenosis} {Risk}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417708/},
	doi = {10.3390/diagnostics13152614},
	abstract = {The carotid artery is a major blood vessel that supplies blood to the brain. Plaque buildup in the arteries can lead to cardiovascular diseases such as atherosclerosis, stroke, ruptured arteries, and even death. Both invasive and non-invasive methods are used to detect plaque buildup in the arteries, with ultrasound imaging being the first line of diagnosis. This paper presents a comprehensive review of the existing literature on ultrasound image analysis methods for detecting and characterizing plaque buildup in the carotid artery. The review includes an in-depth analysis of datasets; image segmentation techniques for the carotid artery plaque area, lumen area, and intima–media thickness (IMT); and plaque measurement, characterization, classification, and stenosis grading using deep learning and machine learning. Additionally, the paper provides an overview of the performance of these methods, including challenges in analysis, and future directions for research.},
	number = {15},
	urldate = {2024-04-18},
	journal = {Diagnostics},
	author = {Ottakath, Najmath and Al-Maadeed, Somaya and Zughaier, Susu M. and Elharrouss, Omar and Mohammed, Hanadi Hassen and Chowdhury, Muhammad E. H. and Bouridane, Ahmed},
	month = aug,
	year = {2023},
	pmid = {37568976},
	pmcid = {PMC10417708},
	pages = {2614},
}

@article{deo_artificial_2024,
	title = {Artificial {Intelligence} and {Machine} {Learning} in {Cardiology}},
	volume = {149},
	url = {https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.123.065469},
	doi = {10.1161/CIRCULATIONAHA.123.065469},
	number = {16},
	urldate = {2024-04-17},
	journal = {Circulation},
	author = {Deo, Rahul C.},
	month = apr,
	year = {2024},
	note = {Publisher: American Heart Association},
	keywords = {artificial intelligence, cardiovascular diseases, machine learning},
	pages = {1235--1237},
}

@article{chao_echocardiography-based_2024,
	title = {Echocardiography-{Based} {Deep} {Learning} {Model} to {Differentiate} {Constrictive} {Pericarditis} and {Restrictive} {Cardiomyopathy}},
	volume = {17},
	url = {https://www.jacc.org/doi/10.1016/j.jcmg.2023.09.011},
	doi = {10.1016/j.jcmg.2023.09.011},
	number = {4},
	urldate = {2024-04-17},
	journal = {JACC: Cardiovascular Imaging},
	author = {Chao, Chieh-Ju and Jeong, Jiwoong and Arsanjani, Reza and Kim, Kihong and Tsai, Yi-Lin and Yu, Wen-Chung and Farina, Juan M. and Mahmoud, Ahmed K. and Ayoub, Chadi and Grogan, Martha and Kane, Garvan C. and Banerjee, Imon and Oh, Jae K.},
	month = apr,
	year = {2024},
	note = {Publisher: American College of Cardiology Foundation},
	keywords = {artificial intelligence, constrictive pericarditis, deep learning, echocardiography, restrictive cardiomyopathy},
	pages = {349--360},
}

@article{narang_utility_2021,
	title = {Utility of a {Deep}-{Learning} {Algorithm} to {Guide} {Novices} to {Acquire} {Echocardiograms} for {Limited} {Diagnostic} {Use}},
	volume = {6},
	issn = {2380-6583},
	url = {https://doi.org/10.1001/jamacardio.2021.0185},
	doi = {10.1001/jamacardio.2021.0185},
	abstract = {Artificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on more than 5 million examples of the outcome of ultrasonographic probe movement on image quality, can provide real-time prescriptive guidance for novice operators to obtain limited diagnostic transthoracic echocardiographic images.To test whether novice users could obtain 10-view transthoracic echocardiographic studies of diagnostic quality using this DL-based software.This prospective, multicenter diagnostic study was conducted in 2 academic hospitals. A cohort of 8 nurses who had not previously conducted echocardiograms was recruited and trained with AI. Each nurse scanned 30 patients aged at least 18 years who were scheduled to undergo a clinically indicated echocardiogram at Northwestern Memorial Hospital or Minneapolis Heart Institute between March and May 2019. These scans were compared with those of sonographers using the same echocardiographic hardware but without AI guidance.Each patient underwent paired limited echocardiograms: one from a nurse without prior echocardiography experience using the DL algorithm and the other from a sonographer without the DL algorithm. Five level 3–trained echocardiographers independently and blindly evaluated each acquisition.Four primary end points were sequentially assessed: qualitative judgement about left ventricular size and function, right ventricular size, and the presence of a pericardial effusion. Secondary end points included 6 other clinical parameters and comparison of scans by nurses vs sonographers.A total of 240 patients (mean [SD] age, 61 [16] years old; 139 men [57.9\%]; 79 [32.9\%] with body mass indexes \&gt;30) completed the study. Eight nurses each scanned 30 patients using the DL algorithm, producing studies judged to be of diagnostic quality for left ventricular size, function, and pericardial effusion in 237 of 240 cases (98.8\%) and right ventricular size in 222 of 240 cases (92.5\%). For the secondary end points, nurse and sonographer scans were not significantly different for most parameters.This DL algorithm allows novices without experience in ultrasonography to obtain diagnostic transthoracic echocardiographic studies for evaluation of left ventricular size and function, right ventricular size, and presence of a nontrivial pericardial effusion, expanding the reach of echocardiography to clinical settings in which immediate interrogation of anatomy and cardiac function is needed and settings with limited resources.},
	number = {6},
	urldate = {2024-04-17},
	journal = {JAMA Cardiology},
	author = {Narang, Akhil and Bae, Richard and Hong, Ha and Thomas, Yngvil and Surette, Samuel and Cadieu, Charles and Chaudhry, Ali and Martin, Randolph P. and McCarthy, Patrick M. and Rubenson, David S. and Goldstein, Steven and Little, Stephen H. and Lang, Roberto M. and Weissman, Neil J. and Thomas, James D.},
	month = jun,
	year = {2021},
	pages = {624--632},
}

@article{zelniker_association_2023,
	title = {Association of {Cardiac} {Biomarkers} {With} {Major} {Adverse} {Cardiovascular} {Events} in {High}-risk {Patients} {With} {Diabetes}: {A} {Secondary} {Analysis} of the {DECLARE}-{TIMI} 58 {Trial}},
	volume = {8},
	issn = {2380-6583},
	shorttitle = {Association of {Cardiac} {Biomarkers} {With} {Major} {Adverse} {Cardiovascular} {Events} in {High}-risk {Patients} {With} {Diabetes}},
	url = {https://doi.org/10.1001/jamacardio.2023.0019},
	doi = {10.1001/jamacardio.2023.0019},
	abstract = {Dapagliflozin reduces the risk of hospitalizations for heart failure and the progression of chronic kidney disease in patients with and without type 2 diabetes (T2D), whereas the effects on reducing atherosclerotic events appear less clear.To explore whether N-terminal pro–B-type natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T (hsTnT) levels can identify a subset of patients with T2D at higher risk and who might benefit more from dapagliflozin with regard to atherosclerotic events.This was a secondary analysis of the DECLARE-TIMI 58 trial, a randomized clinical trial of dapagliflozin in patients with T2D and either multiple risk factors for atherosclerotic cardiovascular disease (ASCVD; approximately 60\%) or established ASCVD (approximately 40\%). All patients with available blood samples at randomization were included in these analyses. Data were collected from May 2013 to September 2018, and data were analyzed from May 2019 to June 2022.Dapagliflozin vs placebo.Major adverse cardiovascular events (MACE), the composite of myocardial infarction, ischemic stroke, or cardiovascular death, which was one of dual primary outcomes of the main trial.Of 14 565 included patients, 9143 (62.8\%) were male, and the mean (SD) age was 63.9 (6.8) years. When tested individually in a multivariable model for MACE risk, NT-proBNP and hsTnT were each significantly associated with the risk of MACE (adjusted hazard ratio [aHR] per 1 SD in log-transformed biomarker: NT-proBNP, 1.62; 95\% CI, 1.49-1.76; hsTnT: 1.59; 95\% CI, 1.46-1.74). The magnitude of the association was similar in patients with ASCVD (NT-proBNP: aHR, 1.60; 95\% CI, 1.45-1.77; hsTnT: aHR, 1.62; 95\% CI, 1.45-1.81) and multiple risk factors for ASCVD (NT-proBNP: aHR, 1.62; 95\% CI, 1.40-1.88; hsTnT: aHR, 1.51; 95\% CI, 1.29-1.77). Moreover, both biomarkers remained independently associated with MACE when both were included in the multivariable model (NT-proBNP: aHR, 1.46; 95\% CI, 1.34-1.60; hsTnT: aHR, 1.39; 95\% CI, 1.26-1.53). Modeled as a continuous variable, baseline biomarker levels did not modify the relative treatment effect of dapagliflozin vs placebo with MACE. However, the relative risk reduction numerically grew with higher biomarker levels, as did the baseline risk. Thus, MACE event rates were nominally lower in dapagliflozin-treated vs placebo-treated patients with biomarker concentrations in the top quartile (NT-proBNP: HR, 0.83; 95\% CI, 0.71-0.97; absolute risk reduction [ARR], 2.4\%; hsTnT: HR, 0.85; 95\% CI, 0.72-0.99; ARR, 2.7\%), whereas there was no significant treatment effect in patients with biomarkers levels in quartiles 1 to 3 (NT-proBNP: HR, 1.02; 95\% CI, 0.88-1.18; ARR, 0\%; hsTnT: HR, 0.97; 95\% CI, 0.84-1.13; ARR, 0.2\%).In this study, NT-proBNP and hsTnT levels were associated with the risk for future cardiovascular events in both primary and secondary prevention patients with T2D. Both cardiac biomarkers were helpful to identify patients at very high risk for atherosclerotic events that may derive reduction in risk of MACE with dapagliflozin.ClinicalTrials.gov Identifier: NCT01730534},
	number = {5},
	urldate = {2024-04-16},
	journal = {JAMA Cardiology},
	author = {Zelniker, Thomas A. and Wiviott, Stephen D. and Mosenzon, Ofri and Goodrich, Erica L. and Jarolim, Petr and Cahn, Avivit and Bhatt, Deepak L. and Leiter, Lawrence A. and McGuire, Darren K. and Wilding, John and Averkov, Oleg and Budaj, Andrzej and Parkhomenko, Alexander and Ray, Kausik K. and Gause-Nilsson, Ingrid and Langkilde, Anna Maria and Fredriksson, Martin and Raz, Itamar and Sabatine, Marc S. and Morrow, David A.},
	month = may,
	year = {2023},
	pages = {503--509},
}

@article{koch_carotid_2022,
	title = {Carotid {Artery} {Ultrasound} in the (peri-) {Arrest} {Setting}—{A} {Prospective} {Pilot} {Study}},
	volume = {11},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2077-0383},
	url = {https://www.mdpi.com/2077-0383/11/2/469},
	doi = {10.3390/jcm11020469},
	abstract = {Point-of-care ultrasounds (US) are used during cardiopulmonary resuscitation (CPR) and after return of spontaneous circulation (ROSC). Carotid ultrasounds are a potential non-invasive monitoring tool for chest compressions, but their general value and feasibility during CPR are not fully determined. In this prospective observational study, we performed carotid US during conventional- and extracorporeal CPR and after ROSC with at least one transverse and coronal image, corresponding loops with and without color doppler, and pulsed-wave doppler loops. The feasibility of carotid US during (peri-)arrest and type and frequency of diagnostic findings were examined. We recruited 16 patients and recorded utilizable US images in 14 cases (88\%; complete imaging protocols in 11 patients [69\%]). In three of all patients (19\%) and in 60\% (3/5) of cases during CPR plus a full imaging protocol, we observed: (i) in one patient a collapse of the common carotid artery linked to hypovolemia, and (ii) in two patients a biphasic flow during CPR linked to prolonged low-flow time prior to admission and adverse outcome. Carotid artery morphology and carotid blood flow characteristics may serve as therapeutic target and prognostic parameters. However, future studies with larger sample sizes are needed.},
	language = {en},
	number = {2},
	urldate = {2024-04-10},
	journal = {Journal of Clinical Medicine},
	author = {Koch, Moritz and Mueller, Matthias and Warenits, Alexandra-Maria and Holzer, Michael and Spiel, Alexander and Schnaubelt, Sebastian},
	month = jan,
	year = {2022},
	note = {Number: 2
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {cardiac arrest, cardiopulmonary resuscitation, carotid ultrasound, emergency medicine, point-of-care ultrasound},
	pages = {469},
}

@article{nisha_deep_2023,
	title = {A {Deep} {Learning} {Framework} for the {Detection} of {Abnormality} in {Cerebral} {Blood} {Flow} {Velocity} {Using} {Transcranial} {Doppler} {Ultrasound}},
	volume = {13},
	issn = {2075-4418},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297317/},
	doi = {10.3390/diagnostics13122000},
	abstract = {Transcranial doppler (TCD) ultrasound is a non-invasive imaging technique that can be used for continuous monitoring of blood flow in the brain through the major cerebral arteries by calculating the cerebral blood flow velocity (CBFV). Since the brain requires a consistent supply of blood to function properly and meet its metabolic demand, a change in CBVF can be an indication of neurological diseases. Depending on the severity of the disease, the symptoms may appear immediately or may appear weeks later. For the early detection of neurological diseases, a classification model is proposed in this study, with the ability to distinguish healthy subjects from critically ill subjects. The TCD ultrasound database used in this study contains signals from the middle cerebral artery (MCA) of 6 healthy subjects and 12 subjects with known neurocritical diseases. The classification model works based on the maximal blood flow velocity waveforms extracted from the TCD ultrasound. Since the signal quality of the recorded TCD ultrasound is highly dependent on the operator’s skillset, a noisy and corrupted signal can exist and can add biases to the classifier. Therefore, a deep learning classifier, trained on a curated and clean biomedical signal can reliably detect neurological diseases. For signal classification, this study proposes a Self-organized Operational Neural Network (Self-ONN)-based deep learning model Self-ResAttentioNet18, which achieves classification accuracy of 96.05\% with precision, recall, f1 score, and specificity of 96.06\%, 96.05\%, 96.06\%, and 96.09\%, respectively. With an area under the ROC curve of 0.99, the model proves its feasibility to confidently classify middle cerebral artery (MCA) waveforms in near real-time.},
	number = {12},
	urldate = {2024-04-09},
	journal = {Diagnostics},
	author = {Nisha, Naima Nasrin and Podder, Kanchon Kanti and Chowdhury, Muhammad E. H. and Rabbani, Mamun and Wadud, Md. Sharjis Ibne and Al-Maadeed, Somaya and Mahmud, Sakib and Khandakar, Amith and Zughaier, Susu M.},
	month = jun,
	year = {2023},
	pmid = {37370895},
	pmcid = {PMC10297317},
	pages = {2000},
}

@article{elola_deep_2019,
	title = {Deep {Neural} {Networks} for {ECG}-{Based} {Pulse} {Detection} during {Out}-of-{Hospital} {Cardiac} {Arrest}},
	volume = {21},
	issn = {1099-4300},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514786/},
	doi = {10.3390/e21030305},
	abstract = {The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80\%) and test (20\%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1\%/92.9\%/93.5\% for the first one, and 95.5\%/91.6\%/93.5\% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.},
	number = {3},
	urldate = {2024-04-05},
	journal = {Entropy},
	author = {Elola, Andoni and Aramendi, Elisabete and Irusta, Unai and Picón, Artzai and Alonso, Erik and Owens, Pamela and Idris, Ahamed},
	month = mar,
	year = {2019},
	pmid = {33267020},
	pmcid = {PMC7514786},
	pages = {305},
}

@article{perkins_european_2021,
	series = {European {Resuscitation} {Council} {Guidelines} for {Resuscitation} 2021},
	title = {European {Resuscitation} {Council} {Guidelines} 2021: {Executive} summary},
	volume = {161},
	issn = {0300-9572},
	shorttitle = {European {Resuscitation} {Council} {Guidelines} 2021},
	url = {https://www.sciencedirect.com/science/article/pii/S0300957221000551},
	doi = {10.1016/j.resuscitation.2021.02.003},
	abstract = {Informed by a series of systematic reviews, scoping reviews and evidence updates from the International Liaison Committee on Resuscitation, the 2021 European Resuscitation Council Guidelines present the most up to date evidence-based guidelines for the practice of resuscitation across Europe. The guidelines cover the epidemiology of cardiac arrest; the role that systems play in saving lives, adult basic life support, adult advanced life support, resuscitation in special circumstances, post resuscitation care, first aid, neonatal life support, paediatric life support, ethics and education.},
	urldate = {2024-04-04},
	journal = {Resuscitation},
	author = {Perkins, Gavin D. and Gräsner, Jan-Thorsen and Semeraro, Federico and Olasveengen, Theresa and Soar, Jasmeet and Lott, Carsten and Van de Voorde, Patrick and Madar, John and Zideman, David and Mentzelopoulos, Spyridon and Bossaert, Leo and Greif, Robert and Monsieurs, Koen and Svavarsdóttir, Hildigunnur and Nolan, Jerry P. and Ainsworth, S. and Akin, S. and Alfonzo, A. and Andres, J. and Attard Montalto, S. and Barelli, A. and Baubin, M. and Behringer, W. and Bein, B. and Biarent, D. and Bingham, R. and Blom, M. and Boccuzzi, A. and Borra, V. and Bossaert, L. and Böttiger, B. W. and Breckwoldt, J. and Brissaud, O. and Burkart, R. and Cariou, A. and Carli, P. and Carmona, F. and Cassan, P. and Castren, M. and Christophides, T. and Cimpoesu, C. D. and Clarens, C. and Conaghan, P. and Couper, K. and Cronberg, T. and De Buck, E. and de Lucas, N. and De Roovere, A. and Deakin, C. D. and Delchef, J. and Dirks, B. and Djakow, J. and Djarv, T. and Druwe, P. and Eldin, G. and Ersdal, H. and Friberg, H. and Genbrugge, C. and Georgiou, M. and Goemans, E. and Gonzalez-Salvado, V. and Gradisek, P. and Graesner, J. T. and Greif, R. and Handley, A. J. and Hassager, C. and Haywood, K. and Heltne, J. K. and Hendrickx, D. and Herlitz, J. and Hinkelbein, J. and Hoffmann, F. and Hunyadi Anticevic, S. and Johannesdottir, G. B. and Khalifa, G. and Klaassen, B. and Koppl, J. and Kreimeier, U. and Kuzovlev, A. and Lauritsen, T. and Lilja, G. and Lippert, F. and Lockey, A. and Lott, C. and Lulic, I. and Maas, M. and Maconochie, I. and Madar, J. and Martinez-Mejias, A. and Masterson, S. and Mentzelopoulos, S. D. and Meyran, D. and Monsieurs, K. G. and Morley, C. and Moulaert, V. R. M. and Mpotos, N. and Nikolaou, N. and Nolan, J. P. and Olasveengen, T. M. and Oliver, E. and Paal, P. and Pellis, T. and Perkins, G. D. and Pflanzl-Knizacek, L. and Pitches, K. and Poole, K. and Raffay, V. and Renier, W. and Ristagno, G. and Roehr, C. C. and Rosell-Ortiz, F. and Rudiger, M. and Safri, A. and Sanchez Santos, L. and Sandroni, C. and Sari, F. and Scapigliati, A. and Schilder, S. and Schlieber, J. and Schnaubelt, S. and Semeraro, F. and Shammet, S. and Singletary, E. M. and Skare, C. and Skrifvars, M. B. and Smyth, M. and Soar, J. and Svavarsdottir, H. and Szczapa, T. and Taccone, F. and Tageldin Mustafa, M. and Te Pas, A. and Thies, K. C. and Tjelmeland, I. B. M. and Trevisanuto, D. and Truhlar, A. and Trummer, G. and Turner, N. M. and Urlesberger, B. and Vaahersalo, J. and Van de Voorde, P. and Van Grootven, H. and Wilkinson, D. and Wnent, J. and Wyllie, J. P. and Yeung, J. and Zideman, D. A.},
	month = apr,
	year = {2021},
	pages = {1--60},
}

@book{oecd_health_2017,
	address = {Paris},
	title = {Health at a {Glance} 2017: {OECD} {Indicators}},
	shorttitle = {Health at a {Glance} 2017},
	url = {https://www.oecd-ilibrary.org/social-issues-migration-health/health-at-a-glance-2017_health_glance-2017-en},
	abstract = {This new edition of Health at a Glance presents the most recent comparable data on the health status of populations and health system performance in OECD countries.},
	language = {en},
	urldate = {2024-04-02},
	publisher = {Organisation for Economic Co-operation and Development},
	author = {{OECD}},
	year = {2017},
}

@article{poelma_ultrasound_2016,
	title = {Ultrasound {Imaging} {Velocimetry}: a review},
	volume = {58},
	issn = {1432-1114},
	shorttitle = {Ultrasound {Imaging} {Velocimetry}},
	url = {https://doi.org/10.1007/s00348-016-2283-9},
	doi = {10.1007/s00348-016-2283-9},
	abstract = {Whole-field velocity measurement techniques based on ultrasound imaging (a.k.a. ‘ultrasound imaging velocimetry’ or ‘echo-PIV’) have received significant attention from the fluid mechanics community in the last decade, in particular because of their ability to obtain velocity fields in flows that elude characterisation by conventional optical methods. In this review, an overview is given of the history, typical components and challenges of these techniques. The basic principles of ultrasound image formation are summarised, as well as various techniques to estimate flow velocities; the emphasis is on correlation-based techniques. Examples are given for a wide range of applications, including in vivo cardiovascular flow measurements, the characterisation of sediment transport and the characterisation of complex non-Newtonian fluids. To conclude, future opportunities are identified. These encompass not just optimisation of the accuracy and dynamic range, but also extension to other application areas.},
	language = {en},
	number = {1},
	urldate = {2024-03-20},
	journal = {Experiments in Fluids},
	author = {Poelma, Christian},
	month = dec,
	year = {2016},
	keywords = {Flow Measurement Technique, Particle Image Velocimetry, Transducer Element, Ultrasound Imaging, Velocimetry},
	pages = {3},
}

@article{sisini_oscillating_2015,
	title = {The {Oscillating} {Component} of the {Internal} {Jugular} {Vein} {Flow}: {The} {Overlooked} {Element} of {Cerebral} {Circulation}},
	volume = {2015},
	shorttitle = {The {Oscillating} {Component} of the {Internal} {Jugular} {Vein} {Flow}},
	doi = {10.1155/2015/170756},
	abstract = {The jugular venous pulse (JVP) provides valuable information about cardiac haemodynamics and filling pressures and is an indirect estimate of the central venous pressure (CVP). Recently it has been proven that JVP can be obtained by measuring the cross-sectional area (CSA) of the IJV on each sonogram of an ultrasound B-mode sonogram sequence. It has also been proven that during its pulsation the IJV is distended and hence that the pressure gradient drives the IJV haemodynamics. If this is true, then it will imply the following: (i) the blood velocity in the IJV is a periodic function of the time with period equal to the cardiac period and (ii) the instantaneous blood velocity is given by a time function that can be derived from a flow-dynamics theory that uses the instantaneous pressure gradient as a parameter. The aim of the present study is to confirm the hypothesis that JVP regulates the IJV blood flow and that pressure waves are transmitted from the heart toward the brain through the IJV wall.},
	journal = {Behavioural Neurology},
	author = {Sisini, Francesco and Toro, Eleuterio and Gambaccini, Mauro and Zamboni, Paolo},
	month = dec,
	year = {2015},
}

@article{zolgharni_automated_2014,
	title = {Automated {Aortic} {Doppler} {Flow} {Tracing} for {Reproducible} {Research} and {Clinical} {Measurements}},
	volume = {33},
	doi = {10.1109/TMI.2014.2303782},
	abstract = {In clinical practice, echocardiographers are often unkeen to make the significant time investment to make additional multiple measurements of Doppler velocity. Main hurdle to obtaining multiple measurements is the time required to manually trace a series of Doppler traces. To make it easier to analyze more beats, we present the description of an application system for automated aortic Doppler envelope quantification, compatible with a range of hardware platforms. It analyses long Doppler strips, spanning many heartbeats, and does not require electrocardiogram to separate individual beats. We tested its measurement of velocity-time-integral and peak-velocity against the reference standard defined as the average of three experts who each made three separate measurements. The automated measurements of velocity-time-integral showed strong correspondence (R(2) = 0.94) and good Bland-Altman agreement (SD = 1.39 cm) with the reference consensus expert values, and indeed performed as well as the individual experts ( R(2) = 0.90 to 0.96, SD = 1.05 to 1.53 cm). The same performance was observed for peak-velocities; ( R(2) = 0.98, SD = 3.07 cm/s) and ( R(2) = 0.93 to 0.98, SD = 2.96 to 5.18 cm/s). This automated technology allows {\textgreater} 10 times as many beats to be analyzed compared to the conventional manual approach. This would make clinical and research protocols more precise for the same operator effort.},
	journal = {IEEE transactions on medical imaging},
	author = {Zolgharni, Massoud and Dhutia, Niti and Cole, Graham and Bahmanyar, Mohammad and Jones, Siana and Sohaib, S M and Tai, Sarah and Willson, Keith and Finegold, Judith and Francis, Darrel},
	month = may,
	year = {2014},
	pages = {1071--82},
}

@article{prasad_hrishi_head_2019,
	title = {Head {Rules} {Over} the {Heart}: {Cardiac} {Manifestations} of {Cerebral} {Disorders}},
	volume = {23},
	issn = {0972-5229},
	shorttitle = {Head {Rules} {Over} the {Heart}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686577/},
	doi = {10.5005/jp-journals-10071-23208},
	abstract = {The Brain-Heart interaction is becoming increasingly important as the underlying pathophysiological mechanisms become better understood. “Neurocardiology” is a new field which explores the pathophysiological interplay of the brain and cardiovascular systems. Brain-heart cross-talk presents as a result of direct stimulation of some areas of the brain, leading to a sympathetic or parasympathetic response or it may present as a result of a neuroendocrine response attributing to a clinical picture of a sympathetic storm. It manifests as cardiac rhythm disturbances, hemodynamic perturbations and in the worst scenarios as cardiac failure and death. Brain-Heart interaction (BHI) is most commonly encountered in traumatic brain injury and subarachnoid hemorrhage presenting as dramatic electrocardiographic changes, neurogenic stunned myocardium or even as ventricular fibrillation. A well-known example of BHI is the panic disorders and emotional stress resulting in Tako-tsubo syndrome giving rise to supraventricular and ventricular tachycardias and transient left ventricular dysfunction., In this review article, we will discuss cardiovascular changes caused due to the disorders of specific brain regions such as the insular cortex, brainstem, prefrontal cortex, hippocampus and the hypothalamus; neuro-cardiac reflexes namely the Cushing's reflex, the Trigemino-cardiac reflex and the Vagal reflex; and other pathological states such as neurogenic stunned myocardium /Takotsubo cardiomyopathy. There is a growing interest among intensivists and anesthesiologists in brain heart interactions as there are an increasing number of cases being reported and there is a need to address unanswered questions, such as the incidence of these interactions, the multifactorial pathogenesis, individual susceptibility, the role of medications, and optimal management.},
	number = {7},
	urldate = {2024-03-19},
	journal = {Indian Journal of Critical Care Medicine : Peer-reviewed, Official Publication of Indian Society of Critical Care Medicine},
	author = {Prasad Hrishi, Ajay and Ruby Lionel, Karen and Prathapadas, Unnikrishnan},
	month = jul,
	year = {2019},
	pmid = {31406441},
	pmcid = {PMC6686577},
	pages = {329--335},
}

@article{elizondo_observed_2019,
	title = {Observed and {Calculated} {Cerebral} {Critical} {Closing} {Pressure} {Are} {Highly} {Correlated} in {Preterm} {Infants}},
	volume = {86},
	issn = {0031-3998},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6658334/},
	doi = {10.1038/s41390-019-0403-y},
	abstract = {Introduction:
Cerebrovascular critical closing pressure (CrCP) is the arterial blood pressure (ABP) at which cerebral blood flow ceases. Preterm ABP is low and close to CrCP. The diastolic closing margin (diastolic ABP minus CrCP), has been associated with intraventricular hemorrhage in preterm infants. CrCP is estimated from middle cerebral artery cerebral blood flow velocity (CBFV) and ABP waveforms. However, these estimations have not been validated due to lack of gold standard. Direct observation of the CrCP in preterm infants with hypotension is an opportunity to validate synchronously estimated CrCP.

Methods:
ABP and CBFV tracings were obtained from 24 extremely low birthweight infants. Recordings where diastolic CBFV was zero were identified. The gold standard CrCP was delineated using piecewise regression of ABP and CBFV values paired by rank ordering and then estimated using a published formula. The measured and estimated values were compared using linear regression and Bland-Altman analysis.

Results:
Linear regression showed a high degree of correlation between measured and calculated CrCP (r2 = 0.93).

Conclusions:
This is the first study to validate a calculated CrCP by comparing it to direct measurements of CrCP from preterm infants when ABP is lower than CrCP.},
	number = {2},
	urldate = {2024-03-19},
	journal = {Pediatric research},
	author = {Elizondo, Leah I. and Rios, Danielle R. and Vu, Eric and Kibler, Kathleen and Easley, R. Blaine and Andropoulos, Dean B. and Acosta, Sebastian and Rusin, Craig G. and Kaiser, Jeffrey R. and Brady, Ken M. and Rhee, Christopher J.},
	month = aug,
	year = {2019},
	pmid = {31003233},
	pmcid = {PMC6658334},
	pages = {242--246},
}

@misc{noauthor_interpretation_nodate,
	title = {Interpretation of {Peripheral} {Arterial} and {Venous} {Doppler} {Waveforms}: {A} {Consensus} {Statement} {From} the {Society} for {Vascular} {Medicine} and {Society} for {Vascular} {Ultrasound} - {Esther} {S}. {H}. {Kim}, {Aditya} {M}. {Sharma}, {Robert} {Scissons}, {David} {Dawson}, {Robert} {T}. {Eberhardt}, {Marie} {Gerhard}-{Herman}, {Joseph} {P}. {Hughes}, {Steve} {Knight}, {Ann} {Marie} {Kupinski}, {Guillaume} {Mahe}, {Marsha} {Neumyer}, {Patricia} {Poe}, {Rita} {Shugart}, {Paul} {Wennberg}, {David} {M}. {Williams}, {R}. {Eugene} {Zierler}, 2020},
	url = {https://journals.sagepub.com/doi/full/10.1177/1544316720943099},
	urldate = {2024-03-19},
}

@article{aziz_ecg-based_2021,
	title = {{ECG}-based machine-learning algorithms for heartbeat classification},
	volume = {11},
	copyright = {2021 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-021-97118-5},
	doi = {10.1038/s41598-021-97118-5},
	abstract = {Electrocardiogram (ECG) signals represent the electrical activity of the human hearts and consist of several waveforms (P, QRS, and T). The duration and shape of each waveform and the distances between different peaks are used to diagnose heart diseases. In this work, to better analyze ECG signals, a new algorithm that exploits two-event related moving-averages (TERMA) and fractional-Fourier-transform (FrFT) algorithms is proposed. The TERMA algorithm specifies certain areas of interest to locate desired peak, while the FrFT rotates ECG signals in the time-frequency plane to manifest the locations of various peaks. The proposed algorithm’s performance outperforms state-of-the-art algorithms. Moreover, to automatically classify heart disease, estimated peaks, durations between different peaks, and other ECG signal features were used to train a machine-learning model. Most of the available studies uses the MIT-BIH database (only 48 patients). However, in this work, the recently reported Shaoxing People’s Hospital (SPH) database, which consists of more than 10,000 patients, was used to train the proposed machine-learning model, which is more realistic for classification. The cross-database training and testing with promising results is the uniqueness of our proposed machine-learning model.},
	language = {en},
	number = {1},
	urldate = {2024-03-19},
	journal = {Scientific Reports},
	author = {Aziz, Saira and Ahmed, Sajid and Alouini, Mohamed-Slim},
	month = sep,
	year = {2021},
	note = {Publisher: Nature Publishing Group},
	keywords = {Cardiology, Diseases, Electrical and electronic engineering, Health care},
	pages = {18738},
}

@inproceedings{zhao_unpaired_2021,
	address = {Nashville, TN, USA},
	title = {Unpaired {Image}-to-{Image} {Translation} via {Latent} {Energy} {Transport}},
	isbn = {978-1-66544-509-2},
	url = {https://ieeexplore.ieee.org/document/9577612/},
	doi = {10.1109/CVPR46437.2021.01615},
	abstract = {Image-to-image translation aims to preserve source contents while translating to discriminative target styles between two visual domains. Most works apply adversarial learning in the ambient image space, which could be computationally expensive and challenging to train. In this paper, we propose to deploy an energy-based model (EBM) in the latent space of a pretrained autoencoder for this task. The pretrained autoencoder serves as both a latent code extractor and an image reconstruction worker. Our model, LETIT1, is based on the assumption that two domains share the same latent space, where latent representation is implicitly decomposed as a content code and a domain-speciﬁc style code. Instead of explicitly extracting the two codes and applying adaptive instance normalization to combine them, our latent EBM can implicitly learn to transport the source style code to the target style code while preserving the content code, an advantage over existing image translation methods. This simpliﬁed solution is also more efﬁcient in the one-sided unpaired image translation setting. Qualitative and quantitative comparisons demonstrate superior translation quality and faithfulness for content preservation. Our model is the ﬁrst to be applicable to 1024×1024-resolution unpaired image translation to the best of our knowledge. Code is available at https://github.com/YangNaruto/latentenergy-transport.},
	language = {en},
	urldate = {2024-03-12},
	booktitle = {2021 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	publisher = {IEEE},
	author = {Zhao, Yang and Chen, Changyou},
	month = jun,
	year = {2021},
	pages = {16413--16422},
}

@article{johnson_artificial_2018,
	title = {Artificial {Intelligence} in {Cardiology}},
	volume = {71},
	url = {https://www.jacc.org/doi/10.1016/j.jacc.2018.03.521},
	doi = {10.1016/j.jacc.2018.03.521},
	number = {23},
	urldate = {2024-03-08},
	journal = {Journal of the American College of Cardiology},
	author = {Johnson, Kipp W. and Torres, Soto Jessica and Glicksberg, Benjamin S. and Shameer, Khader and Miotto, Riccardo and Ali, Mohsin and Ashley, Euan and Dudley, Joel T.},
	month = jun,
	year = {2018},
	note = {Publisher: American College of Cardiology Foundation},
	keywords = {artificial intelligence, cardiology, machine learning, precision medicine},
	pages = {2668--2679},
}

@article{okada_ai_2023,
	title = {{AI} and machine learning in resuscitation: {Ongoing} research, new concepts, and key challenges},
	volume = {15},
	issn = {2666-5204},
	shorttitle = {{AI} and machine learning in resuscitation},
	url = {https://www.sciencedirect.com/science/article/pii/S2666520423000784},
	doi = {10.1016/j.resplu.2023.100435},
	abstract = {Aim
Artificial intelligence (AI) and machine learning (ML) are important areas of computer science that have recently attracted attention for their application to medicine. However, as techniques continue to advance and become more complex, it is increasingly challenging for clinicians to stay abreast of the latest research. This overview aims to translate research concepts and potential concerns to healthcare professionals interested in applying AI and ML to resuscitation research but who are not experts in the field.
Main text
We present various research including prediction models using structured and unstructured data, exploring treatment heterogeneity, reinforcement learning, language processing, and large-scale language models. These studies potentially offer valuable insights for optimizing treatment strategies and clinical workflows. However, implementing AI and ML in clinical settings presents its own set of challenges. The availability of high-quality and reliable data is crucial for developing accurate ML models. A rigorous validation process and the integration of ML into clinical practice is essential for practical implementation. We furthermore highlight the potential risks associated with self-fulfilling prophecies and feedback loops, emphasizing the importance of transparency, interpretability, and trustworthiness in AI and ML models. These issues need to be addressed in order to establish reliable and trustworthy AI and ML models.
Conclusion
In this article, we overview concepts and examples of AI and ML research in the resuscitation field. Moving forward, appropriate understanding of ML and collaboration with relevant experts will be essential for researchers and clinicians to overcome the challenges and harness the full potential of AI and ML in resuscitation.},
	urldate = {2024-03-08},
	journal = {Resuscitation Plus},
	author = {Okada, Yohei and Mertens, Mayli and Liu, Nan and Lam, Sean Shao Wei and Ong, Marcus Eng Hock},
	month = sep,
	year = {2023},
	keywords = {Emergency medicine, Feedback loop, Heterogeneity, Large language model, Natural language processing, Prediction model, Self-fulfilling prophecy},
	pages = {100435},
}

@article{putzer_adrenaline_2021,
	title = {Adrenaline improves regional cerebral blood flow, cerebral oxygenation and cerebral metabolism during {CPR} in a porcine cardiac arrest model using low-flow extracorporeal support},
	volume = {168},
	issn = {0300-9572, 1873-1570},
	url = {https://www.resuscitationjournal.com/article/S0300-9572(21)00289-6/fulltext},
	doi = {10.1016/j.resuscitation.2021.07.036},
	language = {English},
	urldate = {2024-02-22},
	journal = {Resuscitation},
	author = {Putzer, Gabriel and Martini, Judith and Spraider, Patrick and Abram, Julia and Hornung, Rouven and Schmidt, Christine and Bauer, Marlies and Pinggera, Daniel and Krapf, Christoph and Hell, Tobias and Glodny, Bernhard and Helbok, Raimund and Mair, Peter},
	month = nov,
	year = {2021},
	pmid = {34363854},
	note = {Publisher: Elsevier},
	keywords = {Adrenaline, Blood pressure, Cardiac arrest, Cardiopulmonary resuscitation, Cerebral cortex/metabolism, Ecmo, Heart arrest/therapy, Microdialysis/methods, Near infrared spectroscopy, Oxygen/blood, Oxygen/metabolism, Pigs},
	pages = {151--159},
}

@article{marquez_oxygen_2020,
	title = {Oxygen {Exposure} {During} {Cardiopulmonary} {Resuscitation} {Is} {Associated} {With} {Cerebral} {Oxidative} {Injury} in a {Randomized}, {Blinded}, {Controlled}, {Preclinical} {Trial}},
	volume = {9},
	url = {https://www.ahajournals.org/doi/10.1161/JAHA.119.015032},
	doi = {10.1161/JAHA.119.015032},
	abstract = {Background

Hyperoxia during cardiopulmonary resuscitation (CPR) may lead to oxidative injury from mitochondrial‐derived reactive oxygen species, despite guidelines recommending 1.0 inspired oxygen during CPR. We hypothesized exposure to 1.0 inspired oxygen during CPR would result in cerebral hyperoxia, higher mitochondrial‐derived reactive oxygen species, increased oxidative injury, and similar survival compared with those exposed to 21\% oxygen.

Methods and Results

Four‐week‐old piglets (n=25) underwent asphyxial cardiac arrest followed by randomization and blinding to CPR with 0.21 (n=10) or 1.0 inspired oxygen (n=10) through 10 minutes post return of spontaneous circulation. Sham was n=5. Survivors received 4 hours of protocolized postarrest care, whereupon brain was obtained for mitochondrial analysis and neuropathology. Groups were compared using Kruskal‐Wallis test, Wilcoxon rank‐sum test, and generalized estimating equations regression models. Both 1.0 and 0.21 groups were similar in systemic hemodynamics and cerebral blood flow, as well as survival (8/10). The 1.0 animals had relative cerebral hyperoxia during CPR and immediately following return of spontaneous circulation (brain tissue oxygen tension, 85\% [interquartile range, 72\%–120\%] baseline in 0.21 animals versus 697\% [interquartile range, 515\%–721\%] baseline in 1.0 animals; P=0.001 at 10 minutes postarrest). Cerebral mitochondrial reactive oxygen species production was higher in animals treated with 1.0 compared with 0.21 (P{\textless}0.03). Exposure to 1.0 oxygen led to increased cerebral oxidative injury to proteins and lipids, as evidenced by significantly higher protein carbonyls and 4‐hydroxynoneals compared with 0.21 (P{\textless}0.05) and sham (P{\textless}0.001).

Conclusions

Exposure to 1.0 inspired oxygen during CPR caused cerebral hyperoxia during resuscitation, and resultant increased mitochondrial‐derived reactive oxygen species and oxidative injury following cardiac arrest.},
	number = {9},
	urldate = {2024-02-22},
	journal = {Journal of the American Heart Association},
	author = {Marquez, Alexandra M. and Morgan, Ryan W. and Ko, Tiffany and Landis, William P. and Hefti, Marco M. and Mavroudis, Constantine D. and McManus, Meagan J. and Karlsson, Michael and Starr, Jonathan and Roberts, Anna L. and Lin, Yuxi and Nadkarni, Vinay and Licht, Daniel J. and Berg, Robert A. and Sutton, Robert M. and Kilbaugh, Todd J.},
	month = may,
	year = {2020},
	note = {Publisher: Wiley},
	keywords = {brain, cardiac arrest, cardiopulmonary resuscitation, mitochondria, neuroprotection, oxygen},
	pages = {e015032},
}

@article{ho_clinicians_2006,
	title = {A {Clinician}’s {Guide} to {Tissue} {Doppler} {Imaging}},
	volume = {113},
	url = {https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.105.579268},
	doi = {10.1161/CIRCULATIONAHA.105.579268},
	number = {10},
	urldate = {2024-02-19},
	journal = {Circulation},
	author = {Ho, Carolyn Y. and Solomon, Scott D.},
	month = mar,
	year = {2006},
	note = {Publisher: American Heart Association},
	pages = {e396--e398},
}

@article{kording_doppler_2015,
	title = {Doppler ultrasound compared with electrocardiogram and pulse oximetry cardiac triggering: {A} pilot study},
	volume = {74},
	copyright = {© 2014 Wiley Periodicals, Inc.},
	issn = {1522-2594},
	shorttitle = {Doppler ultrasound compared with electrocardiogram and pulse oximetry cardiac triggering},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.25502},
	doi = {10.1002/mrm.25502},
	abstract = {Purpose Accurate triggering of the cardiac cycle is mandatory for optimal image acquisition and thus diagnostic quality in cardiac magnetic resonance imaging. The purpose of this work was to evaluate Doppler ultrasound as an alternative trigger method in cardiac MRI. Methods Steady-state free precession (SSFP) 2D cine CMR was performed in 11 healthy subjects at 1.5T. Doppler ultrasound (DUS), electrocardiogram (ECG) and pulse oximetry (POX) were used for cardiac triggering. DUS peak detection was verified in comparison to ECG. Quantitative analysis of image quality of each gating method was determined by calculating endocardial border sharpness (EBS) and left ventricular (LV) function parameters and compared with ECG. Results Mean difference between DUS and ECG in detected RR intervals was 0.04 ± 63 ms (r = 0.96). Trigger jitter was not different between ECG and DUS (P = 0.15) but significant different between ECG and POX (P = 0.01). EBS was similar between each method (3.1 ± 0.2 / 2.6 ± 0.2 / 2.9 ± 0.2 pixel). Mean differences in stroke volume were not significantly different with −1 ± 7 mL (ECG/DUS, P = 0.9) and 2 ± 10 mL (ECG/POX, P = 0.8). Conclusion Cine cardiac MRI using DUS was successfully demonstrated. DUS triggering is an alternative method for cardiac MRI and may be applied in a clinical setting. Magn Reson Med 74:1257–1265, 2015. © 2014 Wiley Periodicals, Inc.},
	language = {en},
	number = {5},
	urldate = {2024-02-19},
	journal = {Magnetic Resonance in Medicine},
	author = {Kording, Fabian and Schoennagel, Bjoern and Lund, Gunnar and Ueberle, Friedrich and Jung, Caroline and Adam, Gerhard and Yamamura, Jin},
	year = {2015},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.25502},
	keywords = {Doppler ultrasound, cardiac-gated imaging techniques, cine magnetic resonance imaging, computer-assisted image processing, electrocardiogram, oximetry},
	pages = {1257--1265},
}

@incollection{ashley_understanding_2004,
	title = {Understanding the echocardiogram},
	url = {https://www.ncbi.nlm.nih.gov/books/NBK2215/},
	abstract = {Although few generalists actually perform echocardiograms, most order or have to interpret them at some stage. Our aim then is not to explain how to carry out echocardiography, but how to realize its potential and limitations.},
	language = {en},
	urldate = {2024-02-19},
	booktitle = {Cardiology {Explained}},
	publisher = {Remedica},
	author = {Ashley, Euan A. and Niebauer, Josef},
	year = {2004},
}

@article{seitanidis_identification_2022,
	title = {Identification of {Heart} {Arrhythmias} by {Utilizing} a {Deep} {Learning} {Approach} of the {ECG} {Signals} on {Edge} {Devices}},
	volume = {11},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2073-431X},
	url = {https://www.mdpi.com/2073-431X/11/12/176},
	doi = {10.3390/computers11120176},
	abstract = {Accurate and timely detection of cardiac arrhythmias is crucial in reducing treatment times and, ultimately, preventing serious life-threatening complications, such as the incidence of a stroke. This becomes of major importance, especially during the diagnostic process, where there is limited access to cardiologists, such as in hospital emergency departments. The proposed lightweight solution uses a novel classifier, consistently designed and implemented, based on a 2D convolutional neural network (CNN) and properly optimized in terms of storage and computational complexity, thus making it suitable for deployment on edge devices capable of operating in hospital emergency departments, providing privacy, portability, and constant operation. The experiments on the MIT-BIH arrhythmia database, show that the proposed 2D-CNN obtains an overall accuracy of 95.3\%, mean sensitivity of 95.27\%, mean specificity of 98.82\%, and a One-vs-Rest ROC-AUC score of 0.9934. Moreover, the results and metrics based on the NVIDIA® Jetson Nano™ platform show that the proposed method achieved excellent performance and speed, and would be particularly useful in the clinical practice for continuous real-time (RT) monitoring scenarios.},
	language = {en},
	number = {12},
	urldate = {2024-02-19},
	journal = {Computers},
	author = {Seitanidis, Panagiotis and Gialelis, John and Papaconstantinou, Georgia and Moschovas, Alexandros},
	month = dec,
	year = {2022},
	note = {Number: 12
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {arrhythmia, continuous monitoring, deep learning, edge computing, ΕCG},
	pages = {176},
}

@book{rn_nursing_nodate,
	title = {Nursing {Advanced} {Skills}},
	isbn = {978-1-957068-00-8},
	url = {https://wtcs.pressbooks.pub/nursingadvancedskills/},
	language = {en},
	urldate = {2024-02-19},
	author = {Rn), Open Resources for Nursing (Open},
}

@article{rn_73_nodate,
	title = {7.3 {A} {Systematic} {Approach} to {Interpreting} an {ECG}},
	url = {https://wtcs.pressbooks.pub/nursingadvancedskills/chapter/7-3-a-systematic-approach-to-interpreting-an-ecg/},
	language = {en},
	urldate = {2024-02-19},
	author = {Rn), Open Resources for Nursing (Open},
	note = {Book Title: Nursing Advanced Skills},
}

@article{rn_74_nodate,
	title = {7.4 {ECG} {Patterns} and {Dysrhythmias}},
	url = {https://wtcs.pressbooks.pub/nursingadvancedskills/chapter/7-4-ecg-patterns-and-dysrhythmias/},
	language = {en},
	urldate = {2024-02-19},
	author = {Rn), Open Resources for Nursing (Open},
	note = {Book Title: Nursing Advanced Skills},
}

@article{anbalagan_analysis_2023,
	title = {Analysis of various techniques for {ECG} signal in healthcare, past, present, and future},
	volume = {6},
	issn = {2667-0992},
	url = {https://www.sciencedirect.com/science/article/pii/S2667099223000191},
	doi = {10.1016/j.bea.2023.100089},
	abstract = {Cardiovascular diseases are the primary reason for mortality worldwide. As per WHO survey report in 2019, 17.9 million people died due to CVDs, accounting for 32\% of all global deaths. Among these, heart attacks and strokes were responsible for 85\%, whereas CVDs caused 38\% of the premature deaths (under age of 70) affected by non-communicable diseases. The rate of death can be delayed and may be prevented by efficiently analyzing the ECG signals (i.e., captured by a non-invasive method) at the early stage of the disease. QRS complex in ECG provides pivotal information about the heart diseases. Many researchers have analyzed the ECG signal by traditional approach and machine learning methods for identifying the heart disorders. Performance of these techniques depend on accurate detection of different parameters (such as: P-, Q-, R-, S-, T-waveforms, QRS complex duration, R-peak, PR-interval, and RR-interval) from the ECG signals. This review paper provides a detail discussion and comparison of various ECG analysis techniques along with their pros and cons. It summarizes the ECG capturing method, databases available for disease detection \& classification, and performance measures used by the researchers. Based on these, a future road map is suggested for real time ECG analysis (for identifying the heart related conditions) captured from the wearable devices and suggested the precautionary steps by the artificial system and experts. This method will help in identifying the co-relation of heart disorders with other body organs (such as: retina and brain parts) by analyzing ECG, fundus image, and magnetic resonance imaging (MRI) of human brain.},
	urldate = {2024-02-19},
	journal = {Biomedical Engineering Advances},
	author = {Anbalagan, Thivya and Nath, Malaya Kumar and Vijayalakshmi, D. and Anbalagan, Archana},
	month = nov,
	year = {2023},
	keywords = {Arrhythmia, ECG, Heart disorder from fundus image, Machine learning, QRS complex},
	pages = {100089},
}

@article{faldaas_abstract_2023,
	title = {Abstract 222: {Real} {Time} {Feedback} of {Chest} {Compressions} by {Hands}-{Free} {Carotid} {Doppler} in a {Porcine} {Model}},
	volume = {148},
	shorttitle = {Abstract 222},
	url = {https://www.ahajournals.org/doi/10.1161/circ.148.suppl_1.222},
	doi = {10.1161/circ.148.suppl_1.222},
	abstract = {Background: Real-time hemodynamic feedback devices provide information on cardiopulmonary resuscitation (CPR) quality. This can improve hand positioning and technique, thereby maximizing blood flow and potentially improve survival.

Hypothesis: We hypothesize that a newly developed hands-free Doppler for carotid blood flow velocity (RescueDoppler, RD), can identify optimal vs. suboptimal chest compression sites during CPR.

Aim: To assess the potential of the RD device to continuously monitor the effect of each compression by ultrasound metrics and waveforms in a porcine model.

Methods: Ventricular fibrillation was induced in 5 pigs using an ICD. Manual CPR (blinded for hemodynamic data) was performed for 10 seconds at 3 different compression sites in random order (Fig.), and repeated 6 times. We analyzed ultrasound waveforms with Time Average Velocity (TAV) in a linear mixed model with site as fixed effect, and animal and site within animal as random effects. We further compared TAV to invasive systolic blood pressure (SBP) from the contralateral carotid artery.

Results: Data from 5 pigs (mean weight 31.2 kg) and 29 of 30 sequences were included. Overall, blood flow velocity was highest at compression site 2 (TAV 33 cm/s, P{\textless}0.01), but there was significant variability (P{\textless}0.01) in animals (SD= 3.7) and at sites within animal (SD= 7). A compression site yielding the highest TAV (range 19 to 48 cm/s) or lowest TAV (6-25 cm/s) was successfully identified in all animals with corresponding SBPs 50-81 mmHg, and 46-64 mmHg, respectively. The correlation between TAV and SBP was 0.62-0.90 within animals. Conclusions In this animal study, a newly developed hands-free Doppler enabled assessment of blood flow velocity during CPR and detected significant variation according to chest compression site. RD identified the sites with both high and low blood flow velocities and is thus a promising device for effectively identifying the optimal and suboptimal chest compression sites during CPR.

Download figure},
	number = {Suppl\_1},
	urldate = {2024-02-18},
	journal = {Circulation},
	author = {Faldaas, Bjoern and Nielsen, Erik and Storm, Benjamin S and Lappegård, Knut Tore and How, Ole-jakob and Nilsen, Bent Aksel and Kiss, Gabriel and Skogvoll, Eirik and Torp, Hans and Ingul, Charlotte B},
	month = nov,
	year = {2023},
	note = {Publisher: American Heart Association},
	keywords = {Chest compression, Hemodynamics, Ultrasound},
	pages = {A222--A222},
}

@article{ceriello_personalized_2014,
	title = {Personalized therapy algorithms for type 2 diabetes: a phenotype-based approach},
	volume = {7},
	issn = {null},
	shorttitle = {Personalized therapy algorithms for type 2 diabetes},
	url = {https://www.tandfonline.com/doi/abs/10.2147/PGPM.S50288},
	doi = {10.2147/PGPM.S50288},
	abstract = {Type 2 diabetes is a progressive disease with a complex and multifactorial pathophysiology. Patients with type 2 diabetes show a variety of clinical features, including different “phenotypes” of hyperglycemia (eg, fasting/preprandial or postprandial). Thus, the best treatment choice is sometimes difficult to make, and treatment initiation or optimization is postponed. This situation may explain why, despite the existing complex therapeutic armamentarium and guidelines for the treatment of type 2 diabetes, a significant proportion of patients do not have good metabolic control and at risk of developing the late complications of diabetes. The Italian Association of Medical Diabetologists has developed an innovative personalized algorithm for the treatment of type 2 diabetes, which is available online. According to the main features shown by the patient, six algorithms are proposed, according to glycated hemoglobin (HbA1c, ≥9\% or ≤9\%), body mass index (≤30 kg/m2 or ≥30 kg/m2), occupational risk potentially related to hypoglycemia, chronic renal failure, and frail elderly status. Through self-monitoring of blood glucose, patients are phenotyped according to the occurrence of fasting/preprandial or postprandial hyperglycemia. In each of these six algorithms, the gradual choice of treatment is related to the identified phenotype. With one exception, these algorithms contain a stepwise approach for patients with type 2 diabetes who are metformin-intolerant. The glycemic targets (HbA1c, fasting/preprandial and postprandial glycemia) are also personalized. This accessible and easy to use algorithm may help physicians to choose a personalized treatment plan for each patient and to optimize it in a timely manner, thereby lessening clinical inertia. Point your SmartPhone at the code above. If you have a QR code reader the video abstract will appear. Or use: http://dvpr.es/1onVmqB},
	urldate = {2024-01-28},
	journal = {Pharmacogenomics and Personalized Medicine},
	author = {Ceriello, Antonio and Gallo, Marco and Candido, Riccardo and De Micheli, Alberto and Esposito, Katherine and Gentile, Sandro and Medea, Gerardo},
	month = jun,
	year = {2014},
	note = {Publisher: Dove Medical Press
\_eprint: https://www.tandfonline.com/doi/pdf/10.2147/PGPM.S50288},
	keywords = {Italian Association of Medical Diabetologists, Italian algorithm, personalized treatment, treatment guidelines, type 2 diabetes},
	pages = {129--136},
}

@article{oikonomou_machine_2023,
	title = {Machine learning in precision diabetes care and cardiovascular risk prediction},
	volume = {22},
	issn = {1475-2840},
	url = {https://doi.org/10.1186/s12933-023-01985-3},
	doi = {10.1186/s12933-023-01985-3},
	abstract = {Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at increased cardiovascular risk, we offer a broad overview of various data-driven methods and how they may be leveraged in developing predictive models for personalized care. We review existing as well as expected artificial intelligence solutions in the context of diagnosis, prognostication, phenotyping, and treatment of diabetes and its cardiovascular complications. In addition to discussing the key properties of such models that enable their successful application in complex risk prediction, we define challenges that arise from their misuse and the role of methodological standards in overcoming these limitations. We also identify key issues in equity and bias mitigation in healthcare and discuss how the current regulatory framework should ensure the efficacy and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.},
	number = {1},
	urldate = {2024-01-28},
	journal = {Cardiovascular Diabetology},
	author = {Oikonomou, Evangelos K. and Khera, Rohan},
	month = sep,
	year = {2023},
	keywords = {Artificial intelligence, Cardiovascular disease, Diabetes, Digital health, Machine learning, Personalized medicine, Prediction},
	pages = {259},
}

@article{yagin_explainable_2023,
	title = {Explainable {Artificial} {Intelligence} {Paves} the {Way} in {Precision} {Diagnostics} and {Biomarker} {Discovery} for the {Subclass} of {Diabetic} {Retinopathy} in {Type} 2 {Diabetics}},
	volume = {13},
	issn = {2218-1989},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10745306/},
	doi = {10.3390/metabo13121204},
	abstract = {Diabetic retinopathy (DR), a common ocular microvascular complication of diabetes, contributes significantly to diabetes-related vision loss. This study addresses the imperative need for early diagnosis of DR and precise treatment strategies based on the explainable artificial intelligence (XAI) framework. The study integrated clinical, biochemical, and metabolomic biomarkers associated with the following classes: non-DR (NDR), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) in type 2 diabetes (T2D) patients. To create machine learning (ML) models, 10\% of the data was divided into validation sets and 90\% into discovery sets. The validation dataset was used for hyperparameter optimization and feature selection stages, while the discovery dataset was used to measure the performance of the models. A 10-fold cross-validation technique was used to evaluate the performance of ML models. Biomarker discovery was performed using minimum redundancy maximum relevance (mRMR), Boruta, and explainable boosting machine (EBM). The predictive proposed framework compares the results of eXtreme Gradient Boosting (XGBoost), natural gradient boosting for probabilistic prediction (NGBoost), and EBM models in determining the DR subclass. The hyperparameters of the models were optimized using Bayesian optimization. Combining EBM feature selection with XGBoost, the optimal model achieved (91.25 ± 1.88) \% accuracy, (89.33 ± 1.80) \% precision, (91.24 ± 1.67) \% recall, (89.37 ± 1.52) \% F1-Score, and (97.00 ± 0.25) \% the area under the ROC curve (AUROC). According to the EBM explanation, the six most important biomarkers in determining the course of DR were tryptophan (Trp), phosphatidylcholine diacyl C42:2 (PC.aa.C42.2), butyrylcarnitine (C4), tyrosine (Tyr), hexadecanoyl carnitine (C16) and total dimethylarginine (DMA). The identified biomarkers may provide a better understanding of the progression of DR, paving the way for more precise and cost-effective diagnostic and treatment strategies.},
	number = {12},
	urldate = {2024-01-28},
	journal = {Metabolites},
	author = {Yagin, Fatma Hilal and Yasar, Seyma and Gormez, Yasin and Yagin, Burak and Pinar, Abdulvahap and Alkhateeb, Abedalrhman and Ardigò, Luca Paolo},
	month = dec,
	year = {2023},
	pmid = {38132885},
	pmcid = {PMC10745306},
	pages = {1204},
}

@article{dennis_precision_2020,
	title = {Precision {Medicine} in {Type} 2 {Diabetes}: {Using} {Individualized} {Prediction} {Models} to {Optimize} {Selection} of {Treatment}},
	volume = {69},
	issn = {0012-1797},
	shorttitle = {Precision {Medicine} in {Type} 2 {Diabetes}},
	url = {https://doi.org/10.2337/dbi20-0002},
	doi = {10.2337/dbi20-0002},
	abstract = {Despite the known heterogeneity of type 2 diabetes and variable response to glucose lowering medications, current evidence on optimal treatment is predominantly based on average effects in clinical trials rather than individual-level characteristics. A precision medicine approach based on treatment response would aim to improve on this by identifying predictors of differential drug response for people based on their characteristics and then using this information to select optimal treatment. Recent research has demonstrated robust and clinically relevant differential drug response with all noninsulin treatments after metformin (sulfonylureas, thiazolidinediones, dipeptidyl peptidase 4 [DPP-4] inhibitors, glucagon-like peptide 1 [GLP-1] receptor agonists, and sodium–glucose cotransporter 2 [SGLT2] inhibitors) using routinely available clinical features. This Perspective reviews this current evidence and discusses how differences in drug response could inform selection of optimal type 2 diabetes treatment in the near future. It presents a novel framework for developing and testing precision medicine–based strategies to optimize treatment, harnessing existing routine clinical and trial data sources. This framework was recently applied to demonstrate that “subtype” approaches, in which people are classified into subgroups based on features reflecting underlying pathophysiology, are likely to have less clinical utility compared with approaches that combine the same features as continuous measures in probabilistic “individualized prediction” models.},
	number = {10},
	urldate = {2024-01-28},
	journal = {Diabetes},
	author = {Dennis, John M.},
	month = aug,
	year = {2020},
	pages = {2075--2085},
}

@article{johnson_precision_2021,
	title = {Precision {Medicine}, {AI}, and the {Future} of {Personalized} {Health} {Care}},
	volume = {14},
	issn = {1752-8054},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7877825/},
	doi = {10.1111/cts.12884},
	abstract = {The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less‐common responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.},
	number = {1},
	urldate = {2024-01-28},
	journal = {Clinical and Translational Science},
	author = {Johnson, Kevin B. and Wei, Wei‐Qi and Weeraratne, Dilhan and Frisse, Mark E. and Misulis, Karl and Rhee, Kyu and Zhao, Juan and Snowdon, Jane L.},
	month = jan,
	year = {2021},
	pmid = {32961010},
	pmcid = {PMC7877825},
	pages = {86--93},
}

@article{chang_machine_2023,
	title = {Machine learning pre-hospital real-time cardiac arrest outcome prediction ({PReCAP}) using time-adaptive cohort model based on the {Pan}-{Asian} {Resuscitation} {Outcome} {Study}},
	volume = {13},
	copyright = {2023 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-023-45767-z},
	doi = {10.1038/s41598-023-45767-z},
	abstract = {To save time during transport, where resuscitation quality can degrade in a moving ambulance, it would be prudent to continue the resuscitation on scene if there is a high likelihood of ROSC occurring at the scene. We developed the pre-hospital real-time cardiac arrest outcome prediction (PReCAP) model to predict ROSC at the scene using prehospital input variables with time-adaptive cohort. The patient survival at discharge from the emergency department (ED), the 30-day survival rate, and the final Cerebral Performance Category (CPC) were secondary prediction outcomes in this study. The Pan-Asian Resuscitation Outcome Study (PAROS) database, which includes out-of-hospital cardiac arrest (OHCA) patients transferred by emergency medical service in Asia between 2009 and 2018, was utilized for this study. From the variables available in the PAROS database, we selected relevant variables to predict OHCA outcomes. Light gradient-boosting machine (LightGBM) was used to build the PReCAP model. Between 2009 and 2018, 157,654 patients in the PAROS database were enrolled in our study. In terms of prediction of ROSC on scene, the PReCAP had an AUROC score between 0.85 and 0.87. The PReCAP had an AUROC score between 0.91 and 0.93 for predicting survived to discharge from ED, and an AUROC score between 0.80 and 0.86 for predicting the 30-day survival. The PReCAP predicted CPC with an AUROC score ranging from 0.84 to 0.91. The feature importance differed with time in the PReCAP model prediction of ROSC on scene. Using the PAROS database, PReCAP predicted ROSC on scene, survival to discharge from ED, 30-day survival, and CPC for each minute with an AUROC score ranging from 0.8 to 0.93. As this model used a multi-national database, it might be applicable for a variety of environments and populations.},
	language = {en},
	number = {1},
	urldate = {2024-01-28},
	journal = {Scientific Reports},
	author = {Chang, Hansol and Kim, Ji Woong and Jung, Weon and Heo, Sejin and Lee, Se Uk and Kim, Taerim and Hwang, Sung Yeon and Do Shin, Sang and Cha, Won Chul},
	month = nov,
	year = {2023},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Medical research, Prognosis},
	pages = {20344},
}

@article{kwon_deep-learning-based_2019,
	title = {Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes},
	volume = {139},
	issn = {0300-9572},
	url = {https://www.sciencedirect.com/science/article/pii/S0300957219301212},
	doi = {10.1016/j.resuscitation.2019.04.007},
	abstract = {Aim
Out-of-hospital cardiac arrest (OHCA) is a major healthcare burden, and prognosis is critical in decision-making for treatment and the withdrawal of life-sustaining therapy. This study aimed to develop and validate a deep-learning-based out-of-hospital cardiac arrest prognostic system (DCAPS) for predicting neurologic recovery and survival to discharge.
Methods
The study subjects were patients from the Korea OHCA registry who experienced return of spontaneous circulation (ROSC) after OHCA. A total of 36,190 patients were exclusively divided into a set of 28,045 subjects for derivation data and 8,145 subjects for validation data. We used information available for the time of ROSC as predictor variables, and the endpoints were neurologic recovery (cerebral performance category 1 or 2) and survival to discharge. The DCAPS was developed using the derivation data and represented the favorability of prognosis with a score between 0 and 100.
Results
The area under the receiver operating characteristic curve (AUROC) of DCAPS for predicting neurologic recovery for the validation data was 0.953 [95\% confidence interval 0.952–0.954]; these results significantly outperformed those of logistic regression (0.947 [0.943–0.948]), random forest (0.943 [0.942–0.945]), support vector machine (0.930 [0.929–0.932]), and conventional methods of a previous study (0.817 [0.815–0.820]). The AUROC of the DCAPS for survival to discharge was 0.901 [0.900–0.903], and this result significantly outperformed those of other models as well.
Conclusions
The DCAPS predicted neurologic recovery and survival to discharge of OHCA patients accurately and outperformed the conventional method and other machine-learning methods.},
	urldate = {2024-01-28},
	journal = {Resuscitation},
	author = {Kwon, Joon-myoung and Jeon, Ki-Hyun and Kim, Hyue Mee and Kim, Min Jeong and Lim, Sungmin and Kim, Kyung-Hee and Song, Pil Sang and Park, Jinsik and Choi, Rak Kyeong and Oh, Byung-Hee},
	month = jun,
	year = {2019},
	keywords = {Artificial intelligence, Decision support techniques, Machine learning, Neural networks, Out-of-Hospital cardiac arrest, Prognosis},
	pages = {84--91},
}

@article{toy_artificial_2023,
	title = {Artificial intelligence to support out-of-hospital cardiac arrest care: {A} scoping review},
	volume = {16},
	issn = {2666-5204},
	shorttitle = {Artificial intelligence to support out-of-hospital cardiac arrest care},
	url = {https://www.sciencedirect.com/science/article/pii/S2666520423001340},
	doi = {10.1016/j.resplu.2023.100491},
	abstract = {Background
Artificial intelligence (AI) has demonstrated significant potential in supporting emergency medical services personnel during out-of-hospital cardiac arrest (OHCA) care; however, the extent of research evaluating this topic is unknown. This scoping review examines the breadth of literature on the application of AI in early OHCA care.
Methods
We conducted a search of PubMed®, Embase, and Web of Science in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. Articles focused on non-traumatic OHCA and published prior to January 18th, 2023 were included. Studies were excluded if they did not use an AI intervention (including machine learning, deep learning, or natural language processing), or did not utilize data from the prehospital phase of care.
Results
Of 173 unique articles identified, 54 (31\%) were included after screening. Of these studies, 15 (28\%) were from the year 2022 and with an increasing trend annually starting in 2019. The majority were carried out by multinational collaborations (20/54, 38\%) with additional studies from the United States (10/54, 19\%), Korea (5/54, 10\%), and Spain (3/54, 6\%). Studies were classified into three major categories including ECG waveform classification and outcome prediction (24/54, 44\%), early dispatch-level detection and outcome prediction (7/54, 13\%), return of spontaneous circulation and survival outcome prediction (15/54, 20\%), and other (9/54, 16\%). All but one study had a retrospective design.
Conclusions
A small but growing body of literature exists describing the use of AI to augment early OHCA care.},
	urldate = {2024-01-28},
	journal = {Resuscitation Plus},
	author = {Toy, Jake and Bosson, Nichole and Schlesinger, Shira and Gausche-Hill, Marianne and Stratton, Samuel},
	month = dec,
	year = {2023},
	keywords = {Artificial intelligence, Deep learning, Emergency medical services, Machine learning, Medical dispatch, Out-of-hospital cardiac arrest, Prehospital care},
	pages = {100491},
}

@article{krasteva_deep_2023,
	title = {Deep {Learning} {Strategy} for {Sliding} {ECG} {Analysis} during {Cardiopulmonary} {Resuscitation}: {Influence} of the {Hands}-{Off} {Time} on {Accuracy}},
	volume = {23},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {1424-8220},
	shorttitle = {Deep {Learning} {Strategy} for {Sliding} {ECG} {Analysis} during {Cardiopulmonary} {Resuscitation}},
	url = {https://www.mdpi.com/1424-8220/23/9/4500},
	doi = {10.3390/s23094500},
	abstract = {This study aims to present a novel deep learning algorithm for a sliding shock advisory decision during cardiopulmonary resuscitation (CPR) and its performance evaluation as a function of the cumulative hands-off time. We retrospectively used 13,570 CPR episodes from out-of-hospital cardiac arrest (OHCA) interventions reviewed in a period of interest from 30 s before to 10 s after regular analysis of automated external defibrillators (AEDs). Three convolutional neural networks (CNNs) with raw ECG input (duration of 5, 10, and 15 s) were applied for the shock advisory decision during CPR in 26 sequential analyses shifted by 1 s. The start and stop of chest compressions (CC) can occur at arbitrary times in sequential slides; therefore, the sliding hands-off time (sHOT) quantifies the cumulative CC-free portion of the analyzed ECG. An independent test with CPR episodes in 393 ventricular fibrillations (VF), 177 normal sinus rhythms (NSR), 1848 other non-shockable rhythms (ONR), and 3979 asystoles (ASYS) showed a substantial improvement of VF sensitivity when increasing the analysis duration from 5 s to 10 s. Specificity was not dependent on the ECG analysis duration. The 10 s CNN model presented the best performance: 92–94.4\% (VF), 92.2–94\% (ASYS), 96–97\% (ONR), and 98.2–99.5\% (NSR) for sliding decision times during CPR; 98–99\% (VF), 98.2–99.8\% (ASYS), 98.8–99.1 (ONR), and 100\% (NSR) for sliding decision times after end of CPR. We identified the importance of sHOT as a reliable predictor of performance, accounting for the minimal sHOT interval of 2–3 s that provides a reliable rhythm detection satisfying the American Heart Association (AHA) standards for AED rhythm analysis. The presented technology for sliding shock advisory decision during CPR achieved substantial performance improvement in short hands-off periods ({\textgreater}2 s), such as insufflations or pre-shock pauses. The performance was competitive despite 1–2.8\% point lower ASYS detection during CPR than the standard requirement (95\%) for non-noisy ECG signals. The presented deep learning strategy is a basis for improved CPR practices involving both continuous CC and CC with insufflations, associated with minimal CC interruptions for reconfirmation of non-shockable rhythms (minimum hands-off time) and early treatment of VF (minimal pre-shock pauses).},
	language = {en},
	number = {9},
	urldate = {2024-01-23},
	journal = {Sensors},
	author = {Krasteva, Vessela and Didon, Jean-Philippe and Ménétré, Sarah and Jekova, Irena},
	month = jan,
	year = {2023},
	note = {Number: 9
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {CNN, CPR, ECG, VF, arrhythmia, continuous ECG analysis, deep neural networks, out-of-hospital cardiac arrest, shockable rhythm},
	pages = {4500},
}
