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.

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.

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.

First multimodal LLM to unify ECG time series, 12-lead ECG images, and text for grounded, clinician-aligned ECG interpretation. A dual-encoder framework (ECG-CoCa time-series encoder plus a LLaVA-style vision-language backbone) extracts complementary time-series and image features with cross-modal alignment, trained on knowledge-guided instruction data (ECG-Grounding, linking diagnoses to measurable waveform parameters such as QRS/PR intervals) plus the 1.15-million-conversation ECG-Instruct corpus. Introduces the "Grounded ECG Understanding" benchmark and improves predictive performance, explainability, and grounding over prior ECG-language models such as ECG-Chat and PULSE.

Multi-scale ECG-language pretraining model that aligns 12-lead ECG signals with clinical text reports at three granularities -- token, beat, and rhythm level -- rather than a single global embedding. First fine-tunes a cardiology-specialized text encoder to improve understanding of ECG report language, then trains an ECG-FM-initialized ECG encoder against it with hierarchical contrastive supervision. Outperforms prior ECG-language and self-supervised baselines including MERL, ST-MEM, and HeartLang on zero-shot classification, linear probing, and ECG report generation, with especially large gains at low label fractions. Developed at the University of Hong Kong (HKU-MedAI).

Multimodal cardiac MRI foundation model that fuses 3D+T cine CMR (short-axis and long-axis views) with tabular patient health records (demographics, metabolic, and lifestyle factors) from 42,000 UK Biobank participants. Two-stage self-supervised pretraining -- masked-image reconstruction, then imaging-tabular contrastive alignment -- produces representations that transfer to whole-heart segmentation, cardiac phenotype/physiological-feature regression, and cardiac/metabolic disease classification within one unified framework.

ECG foundation model built on the xLSTM (extended LSTM) architecture: a bidirectional stack of nine alternating scalar- and matrix-memory LSTM blocks that scales linearly with sequence length, unlike the quadratic cost of transformer-based ECG models. Pretrained with SimDINOv2, a coding-rate-regularized self-distillation (DINO) objective adapted from computer vision to ECG time series, on roughly 8 million recordings from CODE, INCART, and Chapman-Shaoxing-Ningbo. Introduced alongside BenchECG, a standardized 8-dataset/10-task benchmark, on which xECG achieves the best average rank of any publicly available ECG foundation model, with particular strength on long-context tasks (30-minute ambulatory arrhythmia classification, multi-hour sleep-apnea segmentation) where transformer-based models are computationally limited.

Vision-language model that jointly embeds a cardiac MRI study, treated as video, with the impression section of its clinical report. Combines a video encoder over cine/LGE frame sequences with a Bio+ClinicalBERT text encoder using CLIP-style contrastive training. Supports zero-shot and few-shot classification of cardiomyopathies, amyloidosis, and LV dysfunction, plus image/report retrieval and structured report drafting. Trained on a private, single-institution corpus of roughly 11,000-14,000 CMR study-report pairs from Cleveland Clinic and Case Western.

Foundation model for cine cardiac MRI, self-supervised via masked autoencoding on nearly 75,000 UK Biobank scans. Uses a Vision Transformer with a convolutional stem, unified across long-axis and short-axis views. Fine-tuned checkpoints are released for ventricle and myocardium segmentation, ejection-fraction regression, cardiovascular disease classification, and landmark localization across several public benchmark datasets (ACDC, M&Ms, M&Ms2, EMIDEC, and others).

Dual-encoder single-lead ECG classifier for atrial fibrillation detection that fuses a raw-signal branch with a spectrogram branch via axial attention and a Transformer. Originally developed as a graduate-course project at TU Darmstadt for the 2017 PhysioNet/CinC Challenge, and later extended in a 2023 follow-up study. Released in four sizes up to 130M parameters (S/M/L/XL), alongside a simpler CNN+LSTM variant.

Open ECG foundation model with 90.9M parameters, built on a wav2vec 2.0-style Transformer and pretrained on 1.25-1.5 million ECGs using a hybrid contrastive-and-generative self-supervised objective. Base pretrained weights and MIMIC-IV-ECG-finetuned downstream checkpoints are both released. Developed on the fairseq_signals framework by the University of Toronto / Vector Institute's Wang lab.

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.

Echocardiography foundation model trained with a latent-predictive (V-JEPA2-style) self-supervised objective rather than pixel reconstruction, pretrained on 18 million echocardiograms from 300,000 patients drawn from the public MIMIC-IV-ECHO dataset plus a private multi-site archive - reportedly the largest echo pretraining corpus assembled to date. With a frozen backbone and only lightweight added layers, it outperforms prior echo foundation models by roughly 20% on ejection-fraction estimation and 17% on right-ventricular pressure estimation, reaches strong view-classification accuracy using just 1% of labels, and transfers zero-shot to pediatric echo better than fully fine-tuned baselines. Developed by the University of Toronto's Bo Wang Lab.

End-to-end pipeline for apical-4-chamber echocardiogram videos that segments the left ventricle, estimates ejection fraction on a beat-to-beat basis, and classifies cardiomyopathy with reduced ejection fraction. Combines a DeepLabV3-ResNet50 segmentation model with a 3D CNN (R2+1D/R3D/MC3) initialized on the Kinetics-400 video dataset. Trained on the public EchoNet-Dynamic dataset released alongside it, and one of the most widely reused open echocardiography models since its 2020 Nature publication. Developed by Stanford University.

Treats ECGs as a language: a QRS-Tokenizer converts raw waveforms into discrete heartbeat 'words' from a learned 8,192-entry vocabulary, and a spatio-temporal transformer (ST-ECGFormer) is pretrained via masked-sentence modeling over these tokens. Evaluated for robust, competitive performance across six public ECG datasets and published at ICLR 2025. Developed by Peking University's digital health group, pretrained on MIMIC-IV-ECG.

Multimodal model that learns a shared representation space for ECG signals and their clinical text reports, pretrained on paired MIMIC-IV-ECG recordings and reports. Supports zero-shot ECG classification via text prompts, tested across six public benchmark datasets including PTB-XL and CPSC2018 without any downstream training data. Developed at Imperial College London and published at ICML 2024.