Cedars-Sinai Medical Center (Smidt Heart Institute) / Ouyang Lab
Video-based deep learning model that grades aortic regurgitation (AR) severity—none/trace, mild, moderate, or severe—from color Doppler echocardiography. View-specific R(2+1)D 3D-CNNs analyze five standard transthoracic views (PLAX, PLAX-AV, A3C, A3C-AV, A5C) and their outputs are combined by a maximum-severity rule at the study level. Trained on ~47,600 color Doppler videos from Cedars-Sinai and externally validated at Stanford Healthcare, reaching AUCs of 0.95 for at-least-moderate AR and 0.97 for severe AR internally. Developed by the Ouyang lab at Cedars-Sinai Medical Center.
Architecture
CNN (3D)
View-specific R(2+1)D 3D-CNN classifiers (five views) combined by max-severity ensembling
Framework
PyTorch
Added to catalog
2026-07-22 21:02:46
Research use only
License for model weights only. Associated code may be licensed seperately, check code source for specific terms.
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.
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 regurgitation severity (none/trace, mild, moderate, severe)