Artificial intelligence-enabled echocardiographic assessment of right ventricular function

Right ventricular (RV) function represents an important predictor of morbidity and mortality in various cardiovascular conditions. Nevertheless, its 2D echocardiographic assessment is challenging due to its complex anatomy and location in the chest, resulting in limited inter-observer reproducibility.

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).

For training EchoNet-RV, 7,169 expert-annotated A4C echocardiographic videos were used. EchoNet-RV comprises two major components: one based on an R(2+1)D-18 architecture for spatiotemporal convolution and another based on a DeepLabV3+ architecture with a ResNet-50 backbone for semantic segmentation. The outputs of these two components are then combined to create beat-to-beat predictions of the RVFAC. The model’s performance was evaluated on a hold-out 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 and all-cause death were also analyzed in the first external test set.

EchoNet-RV segmented the RV with Dice coefficients of 0.893 (95% CI: 0.891–0.895), 0.797 (95% CI: 0.796–0.798), and 0.788 (95% CI: 0.785–0.790) and predicted RVFAC with mean absolute errors of 5.795 (95% CI: 5.560–6.031), 5.830 (95% CI: 5.692–5.970), and 6.363 (95% CI: 6.114–6.611) percentage points in the held-out test set and the two external test sets, respectively. In a randomly selected subset of the external test sets (n=500), EchoNet-RV’s prediction error was significantly lower than inter-observer variability (mean absolute difference: 6.126 (95% CI: 5.735–6.563) vs. 9.699 (95% CI: 9.031–10.458) percentage points, p<0.001). Moreover, it identified RVFAC <35% with areas under the receiver operating characteristic curve of 0.859 (95% CI: 0.843–0.876), 0.725 (95% CI: 0.710–0.740), and 0.684 (95% CI: 0.653–0.713) in the three test sets. In the first external test set, predicted RVFAC values were inversely associated with the composite endpoint of heart failure hospitalization and all-cause death (adjusted HR: 0.948 [95% CI: 0.917–0.979], p<0.001), independent of age, sex, cardiovascular risk factors, and left ventricular systolic function.

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.