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.

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.