Fully automatic four-module deep learning pipeline for identifying, segmenting, and Stanford-subtyping aortic dissection (AD) from CT angiography (CTA). A 3D full-resolution nnU-Net first segments the aorta; the segmented boundary is then multi-view projected for AD identification; for AD-positive cases, a second 3D nnU-Net segments the true lumen (TL) and false lumen (FL); finally, a classifier performs Stanford subtyping from multi-view maximum-density projections of the TL/FL. On 386 CTA scans, the pipeline achieved 0.979 accuracy for AD identification, Dice of 0.968 (TL) and 0.971 (FL) for lumen segmentation, and 0.990 accuracy for Stanford subtyping.

Open-source, fully automated framework for end-to-end aortic analysis from CT angiography (CTA), built on top of TotalSegmentator baseline segmentations with additional refinement. AortaExplorer extracts established biomarkers such as diameters across anatomical segments defined by the European Society of Cardiology, and introduces new metrics including aortic tortuosity. Diameter measurements were validated against expert manual readings in more than 10,000 CTA scans from Danish population cohorts, and the tortuosity index's increase with age is consistent with prior literature; the tool reduces per-case analysis time from about 15 minutes to under 5 minutes.

Lightweight convolutional neural network for binary ECG classification that represents ECG beats as spectrograms (via short-time Fourier transform) rather than raw signals, after denoising and frequency filtration to reduce data volume while preserving diagnostically relevant information. Using the large public PTB-XL dataset, the spectrogram-based CNN reached 99.06% accuracy, outperforming an equivalent raw-signal CNN, while also reducing memory usage and computation by avoiding complex architectures; the authors additionally studied the effect of signal up/down-sampling on classification performance.

CNN-based arrhythmia classifier that fuses the magnitude (scalogram) and phase (phasogram) of the continuous wavelet transform (CWT) of ECG heartbeats, rather than relying on magnitude information alone as most prior 2D-representation approaches do. Several fusion strategies (input-level, intermediate-layer, and output-level fusion) were compared on the public PhysioNet MIT-BIH Arrhythmia database. Despite a simple CNN architecture, the best fusion strategy achieved about 98.5% overall accuracy, 98.5% sensitivity and 95.6% specificity, competitive with more complex state-of-the-art approaches.

Fully automatic, open-source deep learning pipeline for estimating left atrial fibrosis from late gadolinium enhancement (LGE) cardiac MRI, built to remove the operator-dependent steps that limit reproducibility of conventional atrial LGE analysis. A multilabel convolutional neural network delineates the left atrial blood pool, pulmonary veins, and mitral valve; these structures are then used to automatically calculate fibrosis burden via established image-intensity-ratio thresholds, without manual tracing. Validated on a 3D LGE-CMR dataset of 207 scans, the pipeline's automatic segmentation achieved a 91% Dice score against manual tracing, and its fully automatic fibrosis quantification closely matched semi-automatic reference methods. The CNN and pipeline are distributed as part of the open-source CemrgApp platform.

Regression-based convolutional neural network that directly estimates the heart-rate-corrected QT interval (QTc) from a 12-lead ECG, aiming to improve on automated QTc measurements from commercial ECG systems, which often diverge from expert readings. QTcNet was trained on 120,300 algorithm-labeled ECGs from an internal hospital cohort and the public MIMIC-IV database (after correcting for a vendor-specific measurement bias), and evaluated against expert QTc measurements in three independent external cohorts (PTB Diagnostic ECG Database, QTcMS, and ECGRDVQ). It roughly halved the mean absolute error compared with standard ECG analysis software (from 23.4ms to 13.4ms across external validation cohorts), with explainability analysis confirming the model focuses on physiologically plausible QRS-onset and T-offset regions.

Deep-learning model that estimates a patient's 10-year risk of major adverse cardiovascular events (MACE) directly from a single routine chest radiograph (CXR), intended as an opportunistic risk-assessment tool when the inputs needed for the standard ASCVD risk calculator (lipids, blood pressure, smoking status, etc.) are missing. A 2D convolutional network takes the CXR image alone as input and outputs a continuous 10-year MACE risk estimate. Developed on 147,801 CXRs from 40,718 participants in the PLCO cancer-screening trial and externally validated in 8,869 outpatients with unknown ASCVD risk and 2,132 with known risk at Mass General Brigham, CXR CVD-Risk identified people at elevated MACE risk (adjusted hazard ratio 1.73 for a >=7.5% predicted risk) and provided added discrimination beyond the traditional ASCVD score.

Automated deep learning workflow that detects and grades tricuspid regurgitation (TR) severity from full transthoracic echocardiography studies. The pipeline first identifies apical-4-chamber (A4C) video clips with color Doppler across the tricuspid valve from a full echo study, then applies a dedicated R(2+1)D video classifier to grade TR severity. Trained on over 2 million echo videos from 47,312 studies at Cedars-Sinai Medical Center and externally validated at Stanford Healthcare, the model identified color-Doppler A4C views with AUC ≥0.999 and detected clinically significant (moderate-or-severe) TR with AUC 0.951 and severe TR with AUC 0.980. Code and trained model weights are released to support prospective evaluation of AI-assisted TR screening.

Open, transparent deep-learning method for measuring left ventricular global longitudinal strain (GLS) from routine 2D echocardiography, built as an alternative to proprietary vendor strain software. Unity-GLS is a multi-image neural network (based on the HigherHRNet-W32 pose-estimation architecture) that identifies the mitral annulus, LV apex, and endocardial curve from a target frame plus six neighbouring frames, across apical 4-, 3-, and 2-chamber views. Validated against multi-expert (11-reader) consensus tracings from 100 echocardiograms in a UK-wide collaborative, Unity-GLS agreed with expert consensus as strongly as individual human experts and two proprietary vendor packages (correlation with consensus: 0.91 vs. 0.73-0.85 for other methods).

Multi-label 12-lead ECG diagnosis model submitted to the PhysioNet/Computing in Cardiology Challenge 2020, built on the same residual 1D CNN family as the authors' earlier Nature Communications model but retrained and validated across the challenge's large, multi-institutional pooled training set (CPSC2018, China 12-Lead ECG Database, St. Petersburg INCART, PTB and PTB-XL, and the Georgia 12-Lead ECG Database). The model uses an unsupervised pretraining stage -- predicting unseen samples of a partially masked ECG signal -- before supervised fine-tuning to jointly detect nine diagnostic classes (atrial fibrillation, first-degree AV block, left and right bundle branch block, normal rhythm, premature atrial/ventricular contraction, and ST-segment depression/elevation). The 2020 Challenge was notable for requiring every team to publicly release both their trained model weights and full training code, making this one of relatively few 12-lead ECG classifiers with an end-to-end reproducible public pipeline.

One of the foundational baseline segmentation networks submitted to the 2017 Automated Cardiac Diagnosis Challenge (ACDC), comparing 2D and 3D convolutional network designs for segmenting the left ventricle cavity, myocardium, and right ventricle cavity from short-axis cine cardiac MRI at end-diastole and end-systole. The accompanying study systematically explored the tradeoffs between 2D and 3D convolutions for this task, finding that, due to the highly anisotropic voxel spacing typical of clinical cine cardiac MRI, 2D networks that treat each slice independently can match or exceed 3D networks while being far cheaper to train. The public code and pretrained weights for the best-performing configuration have served as a widely used, simple baseline for later cardiac MRI segmentation research (including for automatically deriving ventricular volumes and ejection fraction).

nnU-Net-based pipeline for automated detection and sub-classification of acute thoracic aortic dissection (AD) on heterogeneous CT imaging, formulated as a semantic segmentation task rather than direct image classification. The model segments the false lumen (ascending and descending) and the dissection membrane -- along with optional indirect signs such as hemopericardium, aortic wall hematoma, and supra-aortic branch dissection -- and a patient is classified as AD-positive if at least two of the three primary segmented regions exceed a volume threshold tuned via Youden's index; the same pipeline additionally flags Stanford type A dissections. Trained on 157 heterogeneous internal CT studies (not restricted to a single contrast protocol) from Mannheim University Medical Centre and evaluated on an internal held-out test set as well as public external datasets (ImageTBAD and AVT), the model reached an AUROC of 98.7% internally and 97.0% externally, and correctly flagged 93.3% of dissection cases that had not been clinically suspected before imaging. The authors state the trained network will be made publicly available as a non-medical device for further scientific research.

The original U-Net baseline segmentation network introduced alongside the CAMUS (Cardiac Acquisitions for Multi-structure Ultrasound Segmentation) dataset, one of the largest fully open-access, expert-annotated 2D echocardiography benchmarks. The network segments the left ventricle endocardium (LVEndo), left ventricle epicardium/myocardium (LVEpi), and left atrium (LA) from apical 2-chamber and 4-chamber echo views at end-diastole (ED) and end-systole (ES). In the original ten-fold cross-validation benchmark comparing U-Net, U-Net++, Stacked Hourglass, Anatomically Constrained Neural Networks, and classical methods, the U-Net variant (18M parameters) achieved the best overall accuracy, reaching Dice scores of 0.939 (ED) / 0.916 (ES) for LVEndo and 0.954 (ED) / 0.945 (ES) for LVEpi, approaching inter-observer variability. A pretrained checkpoint of this baseline U-Net is distributed via the University of Sherbrooke's vitalab CASTOR project as part of a broader library for building anatomically-constrained cardiac segmentation pipelines.

Two-stage convolutional neural network that automatically detects and anatomically labels coronary artery, thoracic aorta, and cardiac-valve calcifications in low-dose chest CT acquired for lung-cancer screening. A first CNN with a large receptive field (via dilated convolutions) identifies and anatomically labels candidate calcifications; a second CNN filters true positives from the candidates. Trained and evaluated on 1,744 CT scans from the National Lung Screening Trial (NLST), reaching an F1 of 0.89 (soft-filter reconstructions) / 0.84 (sharp-filter reconstructions) for coronary artery calcifications and a linearly-weighted kappa of 0.90-0.91 for per-subject cardiovascular risk categorization versus the manual reference standard.

Dual Bayesian ResNet model for heart murmur detection from multi-location phonocardiogram (PCG) recordings, developed for the George B. Moody PhysioNet Challenge 2022. Each patient's PCG recordings are segmented into overlapping log-mel spectrograms, which are passed through two Bayesian ResNet binary classifiers running simultaneously (present vs. unknown-or-absent, and unknown vs. present-or-absent); the two outputs are aggregated into a patient-level present/unknown/absent murmur classification. An optional second-stage XGBoost model integrates the DBRes output with demographic data and hand-crafted signal features. On the Challenge's official hidden test set, DBRes achieved a weighted accuracy of 0.771 for the murmur-detection task, placing 4th among all teams.

Deep learning pipeline for detection and quantification of abdominal aortic aneurysm (AAA) -- a typically asymptomatic condition often missed incidentally by radiologists -- from abdominal-pelvic CT. A modified 3D U-Net segments the aorta on both contrast and non-contrast CT volumes with a variable number of images, after which an ellipse-fitting post-processing step measures the aortic cross-sectional diameter along the vessel to detect aneurysmal dilation. Trained and validated on 321 abdominal-pelvic CT examinations from Massachusetts General Hospital, the model reached a sensitivity/specificity of 0.91/0.95 on the primary validation set, and 0.85/1.0 on a separate 57-exam generalization test set with different patient demographics and acquisition characteristics; the authors report that DeepAAA exceeded literature-reported radiologist performance for incidental AAA detection.

Deep learning platform for fully automatic segmentation and phenotyping of coronary intravascular ultrasound (IVUS) pullbacks, packaged with a desktop GUI and CLI. A convolutional encoder-decoder network delineates the internal (lumen) and external elastic lamina borders on each cross-sectional IVUS frame; downstream rule-based analysis derives lumen area, plaque area, plaque burden, automatically flags lesions with plaque burden exceeding 40%, and reports minimum lumen area and maximum plaque burden along the pullback. Also supports end-diastolic gating and manual contour editing. Trained on 305 clinical IVUS pullbacks (270 train / 35 validation) from Philips and Boston Scientific catheters at Emory University; downstream evaluations have applied DeepIVUS to tasks such as automated detection of stent underexpansion.

Self-supervised model that performs single-frame digital-subtraction-angiography-style vessel/background separation directly from a single live (non-subtracted) coronary angiogram frame, then supports fine-tuned coronary vessel segmentation. A U-Net-style network is pretrained via an image-to-image translation objective on 58,128 unannotated angiography DICOM series (3,756 patients), then fine-tuned for vessel segmentation on just 40 expert-annotated frames, reaching a Dice of 0.828 on the held-out fine-tuning set and a new state-of-the-art Dice of 0.755 on the public XCAD benchmark. Intended to help clinicians visualize potential stenosis sites without requiring true two-frame digital subtraction acquisition.

End-to-end deep learning model for detecting fetal QRS complexes directly from non-invasive abdominal ECG (aECG) signals, without requiring a separate reference maternal ECG or heavy hand-crafted feature engineering. The model adopts a ResNet architecture built from 1-D octave convolutions (OctConv), which factorize feature maps into high- and low-frequency components to capture multiple temporal frequency scales while reducing memory and compute cost relative to a standard 1-D ResNet; the resulting feature-importance weighting also highlights the signal regions most relevant to each detection. Evaluated on the PhysioNet/CinC Challenge 2013 fetal ECG database (with added Gaussian and motion-artifact noise to mimic real-world recording conditions), the model reached an F1 score of 91.1% while cutting computation by more than 50% for less than a 2% drop in performance versus a non-octave ResNet baseline.

Video-based deep learning system for automated screening of rheumatic heart disease (RHD) from echocardiography, aimed at low-resource settings where RHD -- the most common acquired heart disease in children and young adults worldwide -- is endemic but echocardiography expertise is scarce. A 3D convolutional neural network (C3D) classifies each echo video clip, explicitly modeling temporal information across frames; a supervised meta-classifier then aggregates the per-video predictions from an exam (which may contain many video clips from different views) into a single exam-level RHD diagnosis. Evaluated on 11,646 echocardiography videos from 912 screening exams collected in underserved areas of Brazil and Uganda, the 3D C3D network significantly outperformed a comparison 2D CNN (VGG16) that ignores temporal information, and the learned aggregation model reached 72.77% exam-level diagnostic accuracy, exceeding simple majority voting across a patient's videos.

nnU-Net-based segmentation network that detects and delineates stenotic lesions directly from X-ray coronary angiography frames, developed for the ARCADE (MICCAI 2023) stenosis-detection challenge. A companion model (YOLO-Angio, same team) handles vessel-tree segmentation; StenUNet focuses specifically on pixel-wise localization of stenotic regions. Placed 3rd overall among ARCADE challenge entrants with an F1 score of 0.5348 on the hold-out test set, within 0.0005 of the 2nd-place team.

Dilated U-Net model for fully automatic segmentation of the intima-media complex (IMC) of the common carotid artery on longitudinal B-mode ultrasound images, used to measure carotid intima-media thickness (cIMT) -- a standard imaging biomarker of subclinical atherosclerosis. A far-wall detection step first localizes the region of interest, and the dilated U-Net then segments the near- and far-wall IMC boundaries within it. Trained and evaluated with 5-fold cross-validation on a multicenter database of 2,176 images annotated by two experts, the method reached a mean absolute thickness difference of under 120 micrometres versus the reference annotations -- smaller than the approximately 180-micrometre inter-observer variability -- with a 98.7% fully-automatic success rate (only 1.3% of cases required manual correction).

U-Net-variant segmentation model that identifies and quantifies coronary artery calcium (CAC) directly from routine non-gated, non-contrast chest CT scans -- the kind ordered for lung-cancer screening or unrelated indications rather than a dedicated cardiac scan -- so that the tens of millions of such scans performed annually can be opportunistically screened for cardiovascular risk without any extra imaging. Predicted calcium masks are combined with the CT's Hounsfield units to compute an Agatston-equivalent score. Trained on 446 expert-segmented scans from 98 medical centers across the U.S. Department of Veterans Affairs national health system (capturing substantial heterogeneity in scanners and protocols) and benchmarked against 795 patients with a paired same-year gated CAC study: nongated AI-CAC differentiates zero-vs-nonzero and <100-vs->=100 Agatston categories with 89.4% (F1 0.93) and 87.3% (F1 0.89) accuracy respectively, and its score stratifies 10-year all-cause mortality (CAC 0 vs. >400: 25.4% vs. 60.2%, hazard ratio 3.49) and composite stroke/MI/death risk (33.5% vs. 63.8%, hazard ratio 3.00). In a simulated opportunistic-screening run across 8,052 low-dose CT scans, cardiologists confirmed 99.2% of patients flagged with AI-CAC >400 would benefit from lipid-lowering therapy. Code and trained model weights are both public under an MIT license.

Multi-outcome AI-ECG risk-estimation platform that turns a single 12-lead ECG into a patient-specific survival curve, using a residual convolutional neural network trained with a discrete-time survival loss to predict not just risk but time-to-event. Beyond all-cause and cardiovascular mortality, separately fine-tuned heads predict future ventricular arrhythmia, complete heart block, atrial fibrillation, atherosclerotic cardiovascular disease, heart failure, and (in follow-up work) hypertension -- all from a single resting ECG, including in ECGs a cardiologist would read as normal. Derived on 1.16 million ECGs from 189,539 Beth Israel Deaconess Medical Center patients and externally validated across transnational cohorts in the USA, Brazil (CODE), and the UK (UK Biobank). A variational autoencoder and genome/phenome-wide association analyses were used to show the model's predictions track biologically plausible features (QRS morphology, LV structure/function, and loci linked to cardiac structure, QT interval, and biological aging). NHS trials of AIRE were planned for late 2025. No public code or model weights have been released.

ECG-guided photoplethysmography (PPG) foundation model pretrained on over 100,000 hours of synchronized PPG-ECG recordings from 58,796 subjects across five clinical and wearable sources, using a CLIP-style contrastive alignment framework so the PPG encoder inherits physiologically grounded structure from paired ECG. Achieves state-of-the-art performance on 13 of 15 conventional physiological-analysis tasks across eight datasets, and shows meaningful discriminative capability (AUC >= 0.70) for 307 ICD-10-coded phenotypes across 16 phecode chapters, including many non-cardiovascular conditions.

Multimodal deep-learning framework that estimates and forecasts abnormal laboratory values directly from a 12-lead ECG plus routinely available demographics, biometrics, and vital signs -- reframing dozens of blood tests as binary classification targets predictable from a test that is already fast, non-invasive, and nearly universal in acute care. A structured state-space (S4) encoder processes the raw ECG waveform and is late-fused with an MLP encoder over the tabular metadata; the same architecture is trained both to estimate the closest lab value within 60 minutes of the ECG ('abnormality prediction') and to forecast whether a value will become abnormal 30/60/120 minutes into the future ('abnormality forecasting'). Trained and evaluated on 385,480 linked ECG-lab-value samples from 127,994 MIMIC-IV patients, the model reaches AUROC > 0.7 for 24 distinct lab abnormalities in the prediction setting and 24 in the forecasting setting, spanning cardiac, renal, hematological, metabolic, immunological, and coagulation categories -- with NT-proBNP elevation the best-predicted marker (AUROC 0.90), followed by hemoglobin, albumin, and hematocrit derangements (AUROC > 0.82). Code for dataset construction, training, and evaluation is public under an MIT license; no pretrained model weights are released.

Supervised EfficientNetV2-based 12-lead ECG model trained on over 1 million ECGs from the Montreal Heart Institute to predict 77 cardiac conditions derived from American Heart Association recommendations, plus fine-tuned digital-biomarker heads for reduced LVEF, 5-year atrial-fibrillation risk, and long-QT-syndrome (LQTS) detection/genotyping. Validated on 881,403 ECGs across 11 geographically diverse cohorts (4 public, 7 private health systems), achieving AUROCs above 0.98 for the 77-condition interpretation task while being 60x smaller and 29x faster at inference than its self-supervised DeepECG-SSL counterpart, with up to 9.7x lower CO2 emissions on equivalent tasks.

Self-supervised EfficientNetV2-based 12-lead ECG foundation model pretrained via contrastive learning and masked-lead modeling on 1.9 million ECGs (Montreal Heart Institute plus CODE-15% and MIMIC-IV), then fine-tuned for the same 77-condition ECG interpretation task and digital-biomarker extraction as DeepECG-SL. Outperforms the supervised counterpart on label-scarce digital-biomarker tasks, with the largest gains on LQTS genotype classification (AUROC 0.931 vs. 0.850, n=127 ECGs) and 5-year atrial-fibrillation risk (AUROC 0.742 vs. 0.734, n=132,050 ECGs), and outperforms ECG-FM and ECGFounder on shared external diagnostic classes.

Deep learning framework, developed in collaboration with the MONAI community, for automatic segmentation of tricuspid valve leaflets from transthoracic 3D echocardiograms in children with hypoplastic left heart syndrome (HLHS) and other forms of single-ventricle congenital heart disease, integrated into 3D Slicer via MONAILabel for interactive clinical/research use. Addresses a modality (pediatric 3D echocardiography) and population (single-ventricle congenital heart disease) largely absent from adult-focused cardiac AI models.

Open-weight video-based deep neural network that predicts reduced right-ventricular systolic function (RVSF) directly from routine left and right coronary angiogram videos, enabling real-time RV-dysfunction screening in the catheterization lab when echocardiography is unavailable. Built on an X3D-M spatiotemporal video architecture (Kinetics-400 pretrained) that aggregates per-video probabilities into a study-level normal-vs-reduced RVSF classification, with Grad-CAM/Guided-Backpropagation explainability confirming attention to RV-specific coronary motion rather than left-ventricular signal. Trained on 8,053 angiographic studies from 6,923 Montreal Heart Institute patients (2017-2023), externally validated at UCSF, and prospectively deployed at MHI via the PACS-AI platform, where AI assistance improved reader accuracy from 72.1% to 77.6% for cardiologists and 43.5% to 64.0% for medical students.

Explainable Inception-style 1D CNN for multi-label arrhythmia detection from 12-lead ECGs, integrating Grad-CAM visualization to highlight the waveform segments driving each prediction. Trained on MIMIC-IV-ECG and externally validated on PTB-XL across atrial fibrillation, sinus tachycardia, conduction disturbances (RBBB/LBBB/LAFB), long QT, Wolff-Parkinson-White pattern, and paced-rhythm detection, with all metrics exceeding 90% internally and strong generalization on external validation.

Multi-instance contrastive self-supervised learning framework for echocardiography video representation learning: two distinct videos from the same patient exam are treated as positive pairs (rather than augmentations of a single clip), and a frame-reordering pretext task on temporally shuffled frames encourages the 3D-CNN backbone to learn temporally coherent representations. After self-supervised pretraining on unlabeled echocardiograms, the backbone is efficiently fine-tuned for cardiac disease classification (severe aortic stenosis, left ventricular hypertrophy) using very few labeled examples, outperforming SimCLR, standard multi-instance SimCLR, and Kinetics-400-initialized baselines across a range of training-data ratios.

Predicts cardiac MRI (CMR) tissue-characterization findings -- wall-motion abnormalities and myocardial scar -- directly from standard transthoracic echocardiography videos (A4C/A2C/PLAX views), using a factorized 3D R2+1D convolutional network. Trained and validated on a single-institution cohort of more than 1,400 patients with paired echo and CMR studies within 30 days of each other. Released weights and inference code cover the two binarized outcomes (wall motion, scar); the continuous CMR tissue markers (native T1, T2, ECV) evaluated in the paper are not part of the public release.

Ensemble of 3D convolutional neural networks that detects severe aortic stenosis directly from single-view 2D parasternal long-axis (PLAX) transthoracic echocardiogram videos, without requiring Doppler imaging. Representations are first pretrained with patient-level contrastive self-supervised learning on PLAX clips, then fine-tuned for binary AS classification; the ensemble is externally validated across a temporally-distinct cohort and geographically-distinct cohorts in California and New England. No pretrained weights are released; only the training/evaluation pipeline is public.

Self-supervised foundation model for photoplethysmography (PPG) that explicitly targets the low-signal-quality problem endemic to real-world wearable and bedside PPG, rather than assuming clean input. A Siamese ResNet (SimSiam-style) encoder is pretrained to produce similar representations for temporally-adjacent high- and low-quality PPG segments presumed to share the same underlying physiological state, with curriculum learning that gradually increases the artifact-level gap between paired segments. Pretrained on over 36 million 30-second PPG pairs drawn from more than 24,100 UCSF ICU patients, then fine-tuned and evaluated on six downstream cardiovascular-monitoring datasets/tasks (heart rate, respiratory rate, blood pressure, and atrial-fibrillation detection), where it exceeds prior state-of-the-art by roughly 75% on respiratory-rate estimation and 5% on AF detection, with performance scaling with backbone depth (ResNet152 best). Code and the pretraining dataset are not public; the pretraining data is available upon request under a UCSF-Emory data use agreement.

Self- and weakly-supervised pipeline for left-ventricle segmentation across the full cardiac cycle in apical-4-chamber echocardiography videos. A video segmentation network (2D super-image or 3D U-Net encoder) is first pretrained with a self-supervised temporal-masking objective on largely unannotated echo frames, then fine-tuned with weak supervision from the sparse end-diastole/end-systole frame labels that most echo datasets provide. Achieves 93.3% Dice on EchoNet-Dynamic, outperforming nnU-Net and non-SSL baselines, and generalizes to the external CAMUS dataset. Developed by the BioMedIA group at MBZUAI.

ECG foundation model designed to remain accurate when leads are missing or signals are noisy. A 1D ConvNeXt V2 encoder is trained with a dual-mode self-distillation objective (separate lead-missing and noise "teachers") alongside contrastive alignment to detailed diagnostic-criteria text reports retrieved via a lightweight, LLM-free "Cardiac Feature Retrieval" module. Consistently ranks best or second-best across PTB-XL diagnostic tasks and MIT-BIH arrhythmia classification under original, noisy, lead-missing, and combined-corruption conditions. Developed by FPT Software AI Center and the University of Arkansas.

Video-based deep learning model that grades aortic regurgitation (AR) severity—none/trace, mild, moderate, or severe—from color Doppler echocardiography. View-specific R(2+1)D 3D-CNNs analyze five standard transthoracic views (PLAX, PLAX-AV, A3C, A3C-AV, A5C) and their outputs are combined by a maximum-severity rule at the study level. Trained on ~47,600 color Doppler videos from Cedars-Sinai and externally validated at Stanford Healthcare, reaching AUCs of 0.95 for at-least-moderate AR and 0.97 for severe AR internally. Developed by the Ouyang lab at Cedars-Sinai Medical Center.

Video-based deep learning model that estimates 14 common blood biomarkers and laboratory values—including hemoglobin (anemia), B-type natriuretic peptide (BNP), troponin I, and blood urea nitrogen (BUN)—directly from apical-4-chamber echocardiogram videos. Built on a spatiotemporal convolutional network (R(2+1)D-style) with residual connections that produces beat-by-beat estimates for both regression and abnormality classification. Trained on over 70,000 echocardiograms from Stanford Healthcare and externally validated at Cedars-Sinai, reaching AUCs around 0.80–0.86 for detecting anemia and elevated BNP. Developed by the Ouyang and Zou labs at Stanford University and Cedars-Sinai.

Deep learning model for automated phenotyping of mitral stenosis (MS) from echocardiography. Uses video-based R(2+1)D convolutional networks on color Doppler and B-mode views to identify and grade mitral stenosis severity, following the multi-view valvular-assessment approach of the EchoNet family. Trained and validated on large clinical echocardiography cohorts from Kaiser Permanente Northern California with external testing at Stanford Healthcare and Cedars-Sinai. Developed by the Ouyang lab.

CNN-based image classifier that screens 12-lead ECG images for a signature of transthyretin amyloid cardiomyopathy (ATTR-CM), producing a study-level probability score. Trained on a private Yale New Haven Health System cohort of nuclear-imaging-confirmed ATTR-CM cases and matched controls. Used alongside a companion echocardiography model to track pre-clinical ATTR-CM progression years before it would otherwise be confirmed by nuclear amyloid imaging. Developed by Yale's CarDS Lab and distributed as a packaged research-use executable rather than downloadable weights.

EfficientNet-B3 CNN that detects hypertrophic cardiomyopathy directly from images of printed or scanned 12-lead ECGs, rather than from raw digital waveforms, enabling screening from a photo of a paper tracing. Initialized via self-supervised contrastive pretraining on patient identity, then fine-tuned at Yale New Haven Hospital on over 124,000 ECGs from about 67,000 patients, with HCM status confirmed by cardiac MRI or echocardiography. Externally validated on ECG images from MIMIC-IV, Amsterdam UMC, and UK Biobank. Developed by Yale's CarDS Lab.

Screens 12-lead ECGs for Chagas cardiomyopathy by first pretraining a feature extractor to predict blood-biomarker levels from MIMIC-IV-ECG data, then fine-tuning on Brazilian CODE-15%, SaMi-Trop, and PTB-XL recordings; the final model is a 5-model ensemble. Submitted to the George B. Moody PhysioNet Challenge 2025 (Detection of Chagas Disease from the ECG), where it placed 5th on the official leaderboard. Developed by a team from Akershus University Hospital and the University of Oslo.

Automated pipeline that segments both heart ventricles and tracks their motion throughout the cardiac cycle from short-axis cine cardiac MRI, producing 3D bi-ventricular models with per-vertex wall-thickness and curvature measurements over time. Built on a shape-refined multi-task fully convolutional network, followed by non-rigid registration and mesh-based motion tracking. Trained on roughly 400 manually annotated pulmonary hypertension patients as part of Imperial College London's UK Digital Heart Project, and underlies the related 4Dsurvival cardiac-motion survival-prediction study.

1D residual neural network that screens 10-second, 12-lead ECG tracings for six common abnormalities: first- and second-degree AV block patterns, right and left bundle branch block, sinus bradycardia, atrial fibrillation, and sinus tachycardia. Reported F1 scores above 80% and specificity over 99% when benchmarked against cardiology residents. Developed at Universidade Federal de Minas Gerais and trained on the large Brazilian CODE-15% ECG dataset.

Multi-task model that jointly assesses signal quality and detects atrial fibrillation from wrist-worn wearable photoplethysmography (PPG), pretrained on roughly one million simulated unlabeled signals before fine-tuning on labeled wearable data. Uses a 1D CNN with separate output heads for signal quality and arrhythmia detection. Developed by Stanford's Ashley Lab.

Fully automatic pipeline that localizes the heart, segments coronary calcium, and produces an Agatston-style coronary artery calcium score from gated and non-gated chest/cardiac CT. A three-stage 3D CNN performs each step in sequence. Validated across the Framingham, NLST, PROMISE, and ROMICAT-II cohorts, where the resulting calcium score predicted cardiovascular events with hazard ratios up to 4.3. Developed by the Harvard AIM Lab.

Reconstructs digital 12-lead ECG waveforms from scanned or photographed paper printouts, using an nnU-Net image segmentation model to trace the signal pixels followed by a Hough-transform-based reconstruction pipeline. This is a digitization tool rather than a diagnostic model - it recovers a usable signal from a paper record rather than producing a diagnosis. Won the PhysioNet/Computing in Cardiology Challenge 2024; developed by a team at the University of Oxford.

1D residual neural network that predicts a patient's age directly from a 12-lead ECG; the gap between this predicted 'ECG age' and true chronological age is used as a biomarker of cardiovascular risk and mortality. Trained on the CODE-15% Brazilian ECG dataset, with the original R² of 0.71 later reproduced (R² = 0.70) in an independent German validation cohort. Developed by researchers at Uppsala University and UFMG.

Multi-task 12-lead ECG model with output heads for incident atrial-fibrillation risk (as a survival curve), incident mortality risk, prevalent AF classification, sex classification, and age regression. Built on a 1D CNN over the raw waveform, and developed by the Broad Institute's ML4H group as a successor to their ECG-AI model published in Circulation. Trained on ECGs from UK Biobank and Massachusetts General Hospital, neither of which is publicly released.

Large-scale ECG foundation model pretrained on more than 10 million recordings spanning 150 label categories from the Harvard-Emory ECG Database. Built as a general-purpose feature extractor that can be fine-tuned for arrhythmia detection, demographic inference, and event prediction, and externally validated on MIMIC-IV-ECG and PTB-XL. Also used as the pretrained backbone for downstream clinical models such as Pocket-K, a hyperkalemia detector. Developed by Peking University and Harvard-Emory researchers.

Predicts a patient's age from echocardiogram videos across four standard views (PLAX, A2C, A4C, and subcostal), trained on a private multi-site cohort of over 2.6 million videos from more than 166,000 studies across roughly 90,000 patients. The gap between this AI-predicted age and true chronological age is studied as a marker of accelerated or delayed cardiovascular aging and its relationship to all-cause mortality. Uses a 3D CNN (R(2+1)D) with a separate pretrained model per view. Developed by Cedars-Sinai Medical Center and Stanford's Ouyang lab.

3D CNN that segments seven cardiac substructures - both ventricles, both atria, the LV myocardium, ascending aorta, and pulmonary artery trunk - from cardiac CT angiography. Trained with a hybrid loss function combining multiple segmentation objectives. Developed at CUHK for the MICCAI 2017 Multi-Modality Whole Heart Segmentation (MM-WHS) challenge.

Two-stage pipeline that segments the left atrium and quantifies atrial scar tissue from 3D late-gadolinium-enhancement cardiac MRI, supporting atrial-fibrillation ablation planning. A Multi-Scale Weight Sharing network first delineates the atrial cavity, then a boundary-patch method segments scar tissue around the detected wall. Developed at Queen Mary University of London for the LAScarQS 2022 MICCAI/STACOM segmentation challenge.

Self-supervised ECG representation learned purely from patient identity: the model is trained so that ECGs from the same patient, recorded at different times, map to nearby points in latent space, with no other labels required. Linear models trained on these representations showed a 51% average performance gain over training from scratch across sex classification, age regression, LVH detection, and AF detection. Developed by the Broad Institute's ML4H group and trained on 3.2 million private ECGs from Massachusetts General Hospital; 12-lead, lead-I-only, and lead-II-only checkpoints are all released.

Translates a raw PPG waveform into a full continuous arterial blood-pressure waveform, from which systolic, diastolic, and mean arterial pressure are derived. Uses a two-stage cascaded 1D convolutional network in a U-Net style, with a coarse approximation stage followed by a refinement stage. Meets BHS Grade A and AAMI accuracy standards for diastolic and mean arterial pressure. Developed at BUET.

Reference benchmark suite for the PTB-XL ECG dataset, providing pretrained xresnet1d, InceptionTime, LSTM, and CPC-pretrained models for 71-label, diagnostic, sub-diagnostic, and super-diagnostic classification of ECG findings. Widely used as a standardized baseline for comparing new ECG classification methods. Developed by Strodthoff et al. at Fraunhofer HHI.

One of the first open foundation models for PPG signals, pretrained on over 57,000 hours (20 million segments) of publicly available data using a morphology-aware self-supervised objective. Evaluated across 20 tasks from 10 datasets spanning cardiovascular health, sleep disorders, pregnancy monitoring, and general wellbeing. Developed by Nokia Bell Labs and published at ICLR 2025.

Open-source PPG foundation model pretrained directly on real-world, field-collected wearable data rather than clean clinical signals alone, aiming for better generalization to the noise of free-living conditions. Uses a ResNet-based encoder trained with a relative contrastive (RelCon) self-supervised objective, and is directly benchmarked against PaPaGei. Developed at the University of Illinois Urbana-Champaign and published at UbiComp 2025.

Generative adversarial network that synthesizes realistic 10-second, 12-lead normal-sinus-rhythm ECGs from scratch, without using any real patient data at inference time, enabling privacy-preserving data sharing and augmentation. Uses a U-Net-style 1D deconvolutional generator with a WaveGAN-inspired discriminator. Outperformed a WaveGAN* baseline on the fraction of generated tracings classified as normal sinus rhythm by a commercial ECG interpretation algorithm. Developed by SimulaMet and Oslo Metropolitan University.

Long-standing toolbox for automated segmentation of the ventricles and atria and derivation of cardiac imaging phenotypes from short- and long-axis cine cardiac MRI. Built on a fully convolutional network trained per slice, and widely reused across UK Biobank cardiac imaging studies since its 2018 publication. Developed at Imperial College London.