Artificial Intelligence-Enabled Echocardiographic Assessment of Right Ventricular Function.

ABSTRACT:

Background

Right ventricular (RV) function is an important predictor of morbidity and mortality in various cardiovascular conditions. Nevertheless, its echocardiographic assessment is challenging due to its complex anatomy and location in the chest, resulting in limited inter-observer reproducibility.

Objectives

We aimed to develop a novel deep learning model - EchoNet-RV - to segment the RV in apical 4-chamber view (A4C) echocardiographic videos and estimate RV fractional area change (RVFAC).

Methods

For training EchoNet-RV, 7,169 expert-annotated A4C echocardiographic videos were used. The model's performance was evaluated on a held-out internal test set of 1,320 A4C videos and two international external test sets of 3,107 and 1,077 A4C videos from two separa

SUBMITTER: Tokodi M 

PROVIDER: S-EPMC12870684 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature