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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.