ArticlemedRxiv : the preprint server for health sciences2026
Artificial Intelligence-Enabled Echocardiographic Assessment of Right Ventricular Function.
Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 - 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.
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