Self-supervised learning (SSL) model that identifies chronically stressed mother-fetus dyads from raw maternal abdominal ECG (aECG), which contains both maternal and fetal cardiac signals. Built on a self-supervised representation-learning approach originally developed for ECG-based emotion recognition, the model is pretrained on public ECG datasets and evaluated on a cohort of pregnant women with chronic stress exposure validated by psychological inventory, maternal hair cortisol, and the fetal stress index (FSI). Using maternal ECG alone with the publicly pretrained model, it detected the chronic-stress-exposure group with AUROC 0.982 and predicted psychological stress score (R2 0.943), FSI (R2 0.946), and maternal hair cortisol (R2 0.931).

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).

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.

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.

Open-source, reproducible pipeline for representing 12-lead ECGs as explicit graphs -- nodes per lead-timepatch, with edges encoding established inter-lead spatial relationships (fully-connected limb- and chest-lead subgraphs bridged via leads I, aVF, V4, and V5) -- and classifying them with a Graph Convolutional Network, paired with GNNExplainer to surface which leads and lead-pairs drove each prediction. Evaluated on PTB-XL for five-class diagnostic superclass classification (AUC 0.86) and, with the same architecture, on anteroseptal-vs-inferior myocardial-infarction localization (AUC 0.92), externally validated on the population-based SHIP cohort (AUC 0.87). Explainability analysis showed the GNN's lead attention recovers standard ECG diagnostic criteria (e.g. V1-V3 for anteroseptal MI, II/III/aVF for inferior MI). Developed at University Medical Center Gottingen; code and an example trained checkpoint are released under CC BY-NC 4.0.

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.

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.

Fully automated pipeline that scans a complete transthoracic echocardiogram study, identifies the apical-4-chamber color-Doppler clips showing the mitral valve, and grades mitral regurgitation severity at the study level. Combines a view/valve-presence classifier with a spatiotemporal CNN for severity classification. Trained on a private Cedars-Sinai cohort of 58,614 studies and externally validated on 915 studies from Stanford Healthcare.

Self-supervised foundation model for 12-lead ECGs, pretrained on 9.1 million recordings covering 164 cardiovascular conditions across adult and pediatric cohorts, including single-lead settings. Uses a HuBERT-style Transformer encoder and can be fine-tuned with a simple output layer for diagnosis and event-prediction tasks. Released in small, base, and large (~183M parameter) configurations by researchers at the University of Brescia.

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.