Automated Deep Learning Phenotyping of Tricuspid Regurgitation in Echocardiography.

ABSTRACT:

Importance

Accurate assessment of tricuspid regurgitation (TR) is necessary for identification and risk stratification.

Objective

To design a deep learning computer vision workflow for identifying color Doppler echocardiogram videos and characterizing TR severity.

Design, setting, and participants

An automated deep learning workflow was developed using 47 312 studies (2 079 898 videos) from Cedars-Sinai Medical Center (CSMC) between 2011 and 2021. Data analysis was performed in 2024. The pipeline was tested on a temporally distinct test set of 2462 studies (108 138 videos) obtained in 2022 at CSMC and a geographically distinct cohort of 5549 studies (278 377 videos) from Stanford Healthcare (SHC). Training and validation cohorts contained data from 31 708 patients at

SUBMITTER: Vrudhula A 

PROVIDER: S-EPMC12004246 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature