Cross-modality cardiac image segmentation model that addresses spatial-temporal confounding -- where the anatomy and imaging-modality elements of cardiac images are intertwined across space and time. DCL performs multi-dimensional causal intervention, modeling causal relationships between images and labels as well as causality along the time and space dimensions, integrating historical optimal interventions to transfer knowledge across temporal contexts. A diffusion mechanism further keeps extracted anatomical elements causally invariant across modalities. On cross-modality cardiac images (MR, CT, and ultrasound), DCL achieved a mean Dice of 0.951, outperforming other advanced segmentation methods.

3D convolutional autoencoder that filters reverberation clutter artifacts from transthoracic echocardiography (TTE) video sequences, improving downstream measurements such as speckle-tracking strain. Built on a 3D U-Net-style encoder-decoder with an input-output skip connection to preserve fine structures and attention-gate modules to focus on cluttered regions, the network was trained on synthetic clutter simulated across six ultrasound vendors and generalized well to real in vivo artifactual sequences, substantially reducing the discrepancy between cluttered and clutter-free strain profiles while running in a fraction of a second per sequence.

Supervised contrastive learning framework for robust, fine-grained echocardiographic view classification across 47 clinically meaningful view types (rather than collapsing views into a few broad categories, as most prior work does). Introduces TTE47, the first publicly available benchmark with 47 fine-grained views independently annotated by three experts, enabling rigorous quantification of inter-observer agreement. A tailored contrastive loss produces a feature space that aligns more strongly with underlying anatomy than with any single annotator's labeling style, and the model outperforms cross-entropy and standard supervised-contrastive baselines on both TTE47 and the public TMED-2 benchmark (the latter without dataset-specific pretraining).

Prototype-based neural network for interpretable, uncertainty-aware classification of aortic stenosis (AS) severity from B-mode echocardiography videos. Rather than a black-box prediction, ProtoASNet bases its output on similarity scores between the input video and a set of learned spatio-temporal prototypes (typically highlighting valve calcification and restricted leaflet motion), and uses an abstention loss to flag ambiguous/uncertain cases for expert review. Evaluated on a private clinical dataset and the public TMED-2 dataset, it achieved balanced accuracy of 80.0% (private) and 79.7% (TMED-2), improving to 82.4% when uncertain cases are excluded.

Reinforcement-learning-based unsupervised domain adaptation framework for spatio-temporal (2D+time) echocardiography segmentation, extending the authors' earlier RL4Seg work to full-length video sequences. RL4Seg3D uses a sliding-window approach supporting high-resolution, full-sized inputs, and fuses multiple reward mechanisms to improve segmentation reliability without requiring additional expert annotations in the target domain. Trained and evaluated on a large dataset of over 30,000 echocardiography videos, it outperforms baselines and foundation models on overall segmentation accuracy as well as echocardiography-specific metrics including anatomical/temporal validity and mitral-valve-commissure landmark precision, and supports test-time optimization via calibrated uncertainty estimates.

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.

Multimodal machine learning model that flags patients at risk of transthyretin amyloid cardiomyopathy (ATTR-CM) -- a progressive, underdiagnosed disease with expanding disease-modifying treatment options -- from routinely available ECG waveforms, echocardiographic measurements, demographics, and diagnosis codes for orthopedic manifestations of amyloidosis (e.g. carpal tunnel syndrome, spinal stenosis). Developed on 799 patients with 5-fold cross-validation (AUROC 0.85) and externally validated on 422 patients at a separate site (AUROC 0.82), with consistent accuracy across Hispanic, non-Hispanic Black, and non-Hispanic White patients. In a subsequent nonrandomized, single-system, multisite clinical trial (the Cardiac Amyloidosis Discovery Trial), patients flagged by ATTRACTnet and referred for confirmatory amyloid scintigraphy were positive for ATTR-CM 48% of the time, more than 2.8x the positivity rate of historical (15.3%) and contemporary (17.0%) controls referred by usual clinical judgment (P < .001 for both). This is a proprietary clinical AI program; no public code or model weights have been released.

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.

Open-source pipeline that classifies aortic stenosis (AS) severity from transthoracic echocardiography by combining structural and functional information. Video-based R(2+1)D convolutional networks read six B-mode and color Doppler views while a segmentation model measures peak aortic-jet velocity, and an ensemble integrates these into a final severity prediction. Trained on 210,193 images from Kaiser Permanente Northern California and validated across held-out, temporally distinct, and external Stanford and Cedars-Sinai cohorts, reaching AUCs up to 0.96–0.99 for severe AS. Developed by the Ouyang 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.

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.

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.

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.

Vision-language foundation model that interprets an entire transthoracic echocardiogram study rather than a single view or video: it classifies the view type of every clip, applies view-informed attention across the full study, and generates or retrieves comprehensive study-level interpretations in English or Italian. Pretrained on a private Cedars-Sinai corpus of 12 million echo video-report pairs. Developed by the Smidt Heart Institute and Stanford's Ouyang lab.

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.