Cedars-Sinai Medical Center (Smidt Heart Institute) / Ouyang Lab
Deep learning model for automated phenotyping of mitral stenosis (MS) from echocardiography. Uses video-based R(2+1)D convolutional networks on color Doppler and B-mode views to identify and grade mitral stenosis severity, following the multi-view valvular-assessment approach of the EchoNet family. Trained and validated on large clinical echocardiography cohorts from Kaiser Permanente Northern California with external testing at Stanford Healthcare and Cedars-Sinai. Developed by the Ouyang lab.
Architecture
CNN (3D)
Multi-view R(2+1)D video CNN
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.
Mitral stenosis severity classification