Stanford University / Cedars-Sinai Medical Center (Ouyang Lab)
Model weights not public. Contact creators for more information.
Video-based deep learning model that estimates 14 common blood biomarkers and laboratory values—including hemoglobin (anemia), B-type natriuretic peptide (BNP), troponin I, and blood urea nitrogen (BUN)—directly from apical-4-chamber echocardiogram videos. Built on a spatiotemporal convolutional network (R(2+1)D-style) with residual connections that produces beat-by-beat estimates for both regression and abnormality classification. Trained on over 70,000 echocardiograms from Stanford Healthcare and externally validated at Cedars-Sinai, reaching AUCs around 0.80–0.86 for detecting anemia and elevated BNP. Developed by the Ouyang and Zou labs at Stanford University and Cedars-Sinai.
Architecture
CNN (3D)
R(2+1)D spatiotemporal CNN with residual connections (decomposed 3D convolutions)
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.
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.
Biomarker / laboratory value estimation (e.g., BNP, troponin I, hemoglobin, BUN)
Detection of abnormal laboratory values (e.g., anemia, elevated BNP/troponin I/BUN)