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.

Machine-learning surrogate model for estimating pulsatile hemodynamic fields (velocity, pressure) in coronary arteries from a steady-state computational fluid dynamics (CFD) prior, avoiding the high computational cost of full pulsatile CFD. The model, a neural field conditioned on hemodynamic boundary conditions, is discretisation-independent and can be parametrised with message-passing or self-attention layers by relaxing point-wise action to permutation-equivariance. Evaluated on 74 stenotic coronary arteries from coronary CT angiography (CCTA) with patient-specific pulsatile CFD as ground truth, the model produced accurate, discretisation-independent estimates of pulsatile velocity and pressure fields.

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.

Deep learning strategy for cost-effective, comprehensive cardiac screening from ECG alone, by transferring domain-specific structural information from cardiac magnetic resonance (CMR) imaging into ECG representations. Combines multimodal contrastive learning with masked data modelling during pretraining on paired ECG-CMR data, then uses only ECG at inference. On 40,044 UK Biobank subjects, the multimodal pretraining improved subject-specific CVD risk prediction by up to 12.19% and cardiac phenotype prediction by up to 27.59% versus ECG-only baselines, with learned ECG representations shown to incorporate information from CMR regions of interest.

Self-supervised deep learning model that extracts cardiovascular-risk-relevant patterns from multimodal polysomnography (PSG) signals -- EEG, ECG, and respiratory signals -- without relying on manual sleep-stage annotations. Trained on 4,398 participants, the model derives 'projection scores' by contrasting embeddings from individuals with and without cardiovascular disease (CVD) outcomes. Externally validated in an independent cohort of 1,093 participants, ECG-derived projection scores were predictive of prevalent and incident cardiac conditions (particularly CVD mortality), and combining projection scores with the Framingham Risk Score consistently improved prediction (AUC 0.607-0.965 internally, 0.710-0.807 externally across most outcomes).

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.

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).

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.

AI-driven pipeline for quantitative coronary-stenosis assessment from routine DICOM coronary angiography videos, combining vessel tracking with a video Swin3D transformer trained and validated on 182,418 angiography videos spanning 5 years at the Montreal Heart Institute. Achieves a mean absolute error of 20.15% and a classification AUROC of 0.8294 for stenosis-percentage prediction against cardiologist assessment, with lower inter-rater variability than two expert interventional cardiologists, and can be fine-tuned to quantitative coronary angiography (QCA) data for even lower error (MAE 7.75%).

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.

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.

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 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.

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.

Measures interventricular septum thickness, LV internal diameter, and posterior wall thickness from PLAX echocardiogram videos, then classifies the underlying cause of left ventricular hypertrophy as either cardiac amyloidosis or hypertrophic cardiomyopathy. Combines an atrous-convolution 2D CNN for wall-thickness segmentation with a 3D residual CNN for etiology classification. Trained on 28,201 videos across Stanford, Cedars-Sinai, and the Unity Imaging Collaborative. Developed by Stanford University.

Automates standard echocardiographic measurements from video, pairing a measurement model with a companion segmentation component. Developed by Stanford and Cedars-Sinai's Ouyang lab; public documentation on the exact measurements covered, training data, and validation performance is limited compared to other EchoNet-family models.

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.