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 separate centers. Additionally, the associations between the predicted RVFAC values and the composite endpoint of heart failure hospitalization or all-cause death were also analyzed in the first external test set. Results EchoNet-RV segmented the RV with Dice coefficients of 0.893 (0.891–0.895), 0.797 (0.796–0.798), and 0.788 (0.785–0.790) and predicted RVFAC with mean absolute errors of 5.795 (5.560–6.031), 5.830 (5.692–5.970), and 6.362 (6.064–6.660) percentage points in the held-out test set and the two external test sets, respectively. In 500 randomly selected videos from the external test sets, EchoNet-RV’s prediction error was significantly lower than the inter-observer variability (p