Complete motion-analysis workflow for the left atrium (LA) using 3D Cine MRI, combining an online-learning segmentation network with an image-registration network to compute LA displacement vector fields (DVF) and principal strains across the cardiac cycle. Validated on 10 healthy volunteers and 8 cardiovascular disease patients, Aladdin accurately tracks LA wall motion and can identify regional deformation abnormalities that may indicate focal pathology, agreeing well with 2D Cine MRI global function estimates.

Self-supervised multi-encoder autoencoder (MEAE) that separates heartbeat-related source signals from noisy photoplethysmogram (PPG) via blind source separation, improving heart-rate detection without requiring any pre-processing or manual data selection. Trained entirely on PPG signals from a large open polysomnography database (with no cleaning or curation), the model is then applied to a noisy real-world PPG dataset collected during daily activities of 9 subjects and a surgical dataset of 4,681 patients; the extracted heartbeat-related source signal significantly improves heart-rate detection accuracy compared with using the raw PPG signal directly.

Open-source, vendor-agnostic deep learning pipeline that retrospectively measures left ventricular global longitudinal strain (GLS) from routine apical-4-chamber echocardiography B-mode video, without requiring speckle-tracking software or manual tracing. The pipeline reuses EchoNet-Dynamic's LV semantic-segmentation network to trace the LV endocardial border frame-by-frame, then measures the frame-to-frame change in traced myocardial length across the cardiac cycle to derive GLS. In external validation against a large 3D-echocardiography-derived GLS dataset and a prospective two-sonographer, two-vendor repeated-measures study, the automated strain measurement showed lower inter- and intra-measurement variability than human readers and moderate agreement with reference speckle-tracking strain (ICC 0.58), while being robust to image-quality differences and vendor.

Pediatric-specific extension of EchoNet-Dynamic: a video-based deep learning model that segments the left ventricle and estimates ejection fraction (EF) from apical-4-chamber (A4C) and parasternal short-axis (PSAX) pediatric echocardiogram clips. Because adult-trained echo models generalize poorly to children (who vary widely in heart size, rate, and image quality), EchoNet-Peds was trained from scratch on a dedicated pediatric video dataset. It segments the LV with a Dice similarity coefficient of 0.89 in both views, estimates EF with a mean absolute error of 3.66%, and identifies pediatric systolic dysfunction with an AUC of 0.95, significantly outperforming an adult-trained model applied to the same pediatric data.

Deep learning model that non-invasively estimates cardiac output (CO) from wearable seismocardiography (SCG), a single-lead ECG, and body mass index (BMI), as a potential alternative to invasive right heart catheterization (RHC). Parallel 1D-CNN branches extract features from the SCG and ECG waveforms, which are fused with BMI and passed through a lightweight regression head to predict CO directly. Trained and evaluated via leave-pair-out cross-validation on 73 heart-failure patients (83 RHC encounters) from an open PhysioNet dataset, the model achieved an RMSE of 1.00 L/min (22%) and Pearson correlation of 0.75 versus catheterization-derived CO, with particularly strong performance in low-output states.

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-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.

Foundation vision-language model for cardiac MRI that learns pathophysiological visual representations directly from the natural-language radiology reports accompanying each scan, rather than from hand-labeled targets. A Multi-scale Vision Transformer (MViT, Kinetics-400-initialized) video encoder for cine CMR sequences is contrastively pretrained (InfoNCE) against a PubMed-pretrained BERT text encoder over 19,041 multi-institutional CMR studies. The frozen vision encoder transfers with strong performance to left-ventricular ejection-fraction regression (MAE 3.34% on a UK Biobank hold-out of ~4,259-45,623 participants) and detecting HFrEF (LVEF<40%, AUC 0.880), and the paper reports emergent zero-/few-shot performance across 39 cardiac and non-cardiac conditions including cardiac amyloidosis and hypertrophic cardiomyopathy. Code and pretrained MViT encoder weights are both released (Hugging Face, CC BY-NC 4.0).

Vision Transformer-based classifier that detects abnormal left-ventricular ejection fraction (LVEF < 50%) directly from static, non-gated, non-contrast chest CT scans -- an imaging modality ordered for unrelated indications (lung cancer screening, pulmonary embolism, trauma) in over 80 million US exams a year -- as a form of opportunistic heart-failure screening. Fine-tunes the encoder of the CT-ViT (GenerateCT) framework, with separate spatial (axial-plane) and z-axis (slice-wise) self-attention blocks, on 3D CT volumes paired with echocardiogram-derived LVEF labels from 25,948 Columbia University studies; reaches an AUROC of 0.786 on a held-out test set and 0.762 on external validation at Weill Cornell Medicine, clearly outperforming demographic/diagnosis-code-only baselines (Random Forest, XGBoost, AUROC 0.54-0.61). On a radiologist-comparison subset, the model's weighted F1 (0.80-0.81) exceeded two board-certified thoracic radiologists (0.62-0.80) at a small fraction of the interpretation time. Grad-CAM saliency maps highlighted clinically sensible correlates of reduced LVEF (cardiomegaly, dilated superior vena cava, calcified ascending aorta, pacemaker hardware, pulmonary edema). No public code or model weights have been released.

Multi-view foundation model for coronary angiography trained with video-text contrastive learning on 203,808 angiography videos from 28,117 patients across 32,473 studies at the Montreal Heart Institute, externally validated on 4,249 studies from UCSF. Integrates multiple angiographic projections with attention-based pooling for study-level assessment spanning diagnostic, prognostic, and disease-progression tasks: significant-stenosis detection (AUROC 0.888 internal / 0.89 external), stenosis-percentage estimation (MAE 13.6% vs. 19.0% for clinical reports), chronic total occlusion, intracoronary thrombus, and coronary calcification detection. Transfer learning further enables one-year MACE prediction (AUROC 0.79) and LVEF estimation (MAE 7.3%) from the same angiography embeddings, with a mean in-hospital inference time of 4.2 seconds.

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.

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.

Framework for training an echocardiography left-ventricle segmentation model purely by data-free knowledge distillation: a ConvLSTM-based student network learns to reproduce the masks produced by an EchoNet-Dynamic (DeepLabV3-ResNet50) teacher on entirely synthetic echo videos, with no real labeled data or even real videos required. Achieves state-of-the-art results identifying end-diastolic/end-systolic frames, reaching segmentation quality close to real-data training with substantially fewer weights; also introduces a human-annotation-free evaluation method using a large auxiliary model.

Adapts the Segment Anything Model (SAM) to echocardiography video segmentation by giving it a space-time memory that carries both spatial and temporal cues, so that only the first frame of a video needs an external point prompt and every subsequent frame is segmented from a propagated memory prompt instead. A memory reinforcement mechanism uses each frame's predicted mask to suppress speckle-noise features before they are written back into memory, addressing a key failure mode of naively adapting video object segmentation (e.g. XMem) to noisy ultrasound. Built on SAMUS (an ultrasound-adapted SAM) with a frozen SAM backbone and only the image-encoder adapter layers trained. On the semi-supervised CAMUS and EchoNet-Dynamic benchmarks (only end-diastole/end-systole frames labeled), MemSAM reaches 93.3% and 92.8% mean Dice respectively, outperforming UNet, SwinUNet, H2Former, and prior medical-SAM adaptations (MedSAM, MSA, SAMed, SonoSAM, SAMUS) with far fewer prompts, and derives LVEF (via Simpson's biplane method of disks) with a Pearson correlation of 78.9% against ground truth on CAMUS. Training/inference code is public (MIT license); only the starting SAM ViT-B checkpoint is linked for download, not a separately released fine-tuned MemSAM checkpoint.

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.

Foundation model that encodes single-lead (lead I) ECGs with information from paired transthoracic echocardiography reports, aimed at label-efficient screening for structural heart disease (SHD) on wearable and portable single-lead ECG devices. A 7-layer 1D-CNN ECG encoder and a RoBERTa-based text encoder are contrastively pretrained (CLIP-style) on 194,551 ECG-echo report pairs from 77,378 adults in the Yale New Haven Health System, then the ECG encoder is fine-tuned on a temporally-distinct cohort to detect reduced LVEF, diastolic dysfunction, and a composite SHD label. Matches a randomly-initialized CNN at full training-data volume but substantially outperforms it in label-scarce regimes (e.g. with only 0.5% of labeled data).

Fully automated deep learning workflow for characterizing cardiac mechanics from balanced steady-state free-precession (bSSFP) cine cardiac MRI. It decouples two convolutional networks—a segmentation net (CarSON) and a 3D motion-estimation net (CarMEN)—to derive left- and right-ventricular volumes plus global and regional myocardial strain and strain rate without manual tracing. Trained and validated on healthy and cardiovascular-disease subjects and shown to be robust across MRI vendors, with excellent intra-scanner repeatability for strain. Developed at Massachusetts General Hospital and the Harvard-MIT Division of Health Sciences and Technology.

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).

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.

Vision-language foundation model fine-tuned from CLIP on more than one million private echocardiogram video-report pairs, enabling zero-shot cardiac function assessment, device identification, and image/text retrieval without task-specific training. Combines a ConvNeXt-Base video encoder with a GPT-2-style text encoder under contrastive pretraining. Training data is private, but model weights and code are public. Developed by Cedars-Sinai's Ouyang lab.

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.

CLIP-style model that aligns 12-lead ECG signals with free-text echocardiography reports for zero-shot detection of structural heart disease directly from an ECG. Extends the MERL framework, and was trained on 45,016 paired ECG-echo reports from two Hong Kong hospitals, with external validation on the public EchoNext dataset from Columbia University. Developed by researchers at the University of Hong Kong and Imperial College London; described in a 2025 medRxiv preprint.

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.

View-agnostic, multi-task model that performs 39 different echocardiographic reporting tasks - covering chamber size and function, valve disease, and more - from any combination of views, aggregating clip-level predictions up to the study level. Combines a ConvNeXt-Tiny frame encoder with a temporal Transformer and separate output heads per task. Trained on private Yale-New Haven Health System echo videos and published in JAMA in 2025 by Yale's CarDS Lab.