Cedars-Sinai Medical Center (Smidt Heart Institute) / Ouyang Lab
Predicts cardiac MRI (CMR) tissue-characterization findings -- wall-motion abnormalities and myocardial scar -- directly from standard transthoracic echocardiography videos (A4C/A2C/PLAX views), using a factorized 3D R2+1D convolutional network. Trained and validated on a single-institution cohort of more than 1,400 patients with paired echo and CMR studies within 30 days of each other. Released weights and inference code cover the two binarized outcomes (wall motion, scar); the continuous CMR tissue markers (native T1, T2, ECV) evaluated in the paper are not part of the public release.
Architecture
CNN (3D)
R2+1D 3D-CNN (factorized spatial + temporal convolutions) applied to A4C/A2C/PLAX echocardiography video clips
Framework
PyTorch
Added to catalog
2026-08-10
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.
Myocardial scar presence, predicted from echo (CMR-confirmed label)
Wall motion abnormality presence, predicted from echo (CMR-confirmed label)