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).

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.

General-purpose vision foundation model for echocardiography, pretrained with a masked autoencoder combined with a periodic contrastive loss designed around the cyclical nature of cardiac motion. Validated on chamber segmentation, view classification, and disease detection, with its largest advantage over non-pretrained baselines and natural-image models like SAM appearing in low-label settings. Pretrained on roughly 290,000 echo clips from a mix of internal and public sources. Developed by Massachusetts General Hospital and Harvard Medical School.

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.

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.