Cedars-Sinai Medical Center (Smidt Heart Institute) / Ouyang Lab
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.
Architecture
Hybrid
Ensemble of multi-view R(2+1)D video CNNs plus a segmentation model for aortic peak-velocity measurement
Framework
PyTorch
Added to catalog
2026-07-22 21:02:46
Cedars-Sinai Medical Center Echocardiography Dataset
CSMC (Cedars-Sinai Medical Center) clinical echocardiography cohort: 877,983 individual sonographer measurements spanning 9 B-mode and 9 Doppler measurement types, drawn from 155,215 studies.
Kaiser Permanente Northern California Echocardiography
Large clinical transthoracic echocardiography cohort from Kaiser Permanente Northern California; 210,193 videos from 16,076 studies used to train EchoNet-AS. Detailed demographics not reported.
Stanford Healthcare Echocardiography
Clinical transthoracic echocardiography cohort from Stanford Healthcare (SHC); used to train EchoNet-Labs (70,066 videos / 39,460 patients) and as an external validation set for several EchoNet valvular models. Detailed demographics not reported.
Aortic stenosis severity classification
Aortic valve segmentation for automated peak-velocity measurement