Artificial Intelligence-Enabled Echocardiographic Assessment of Right Ventricular Function

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<0.001). Moreover, it identified RVFAC <35% with areas under the receiver operating characteristic curve of 0.859 (0.843–0.876), 0.725 (0.710–0.740), and 0.684 (0.653–0.713) in the three test sets. EchoNet-RV also outperformed two multi-task models, EchoPrime and PanEcho, in estimating RVFAC and identifying RV dysfunction in the external test sets. In the first external test set, predicted RVFAC values were inversely associated with the composite endpoint (adjusted HR: 0.948 [0.917–0.979], p<0.001), independent of age, sex, cardiovascular risk factors, and left ventricular systolic function. Conclusions EchoNet-RV enables the rapid and automated assessment of RVFAC, with strong potential to become a valuable tool for the echocardiographic evaluation of RV function and disease surveillance. CONDENSED ABSTRACT In this study, we developed EchoNet-RV, an echocardiography-based DL model for automated RV segmentation and RVFAC estimation, and evaluated its performance on two international external datasets. EchoNet-RV demonstrated robust performance in RV segmentation, RVFAC estimation, and RV dysfunction detection, with prediction errors significantly lower than inter-observer variability. It also outperformed two multi-task models, EchoPrime and PanEcho, in estimating RVFAC and identifying RV dysfunction. Moreover, the model’s predictions were also associated with adverse clinical outcomes. EchoNet-RV enables rapid and automated RVFAC assessment, with strong potential to become a valuable tool for the echocardiographic evaluation of RV function and disease surveillance.