Automated Aortic Regurgitation Detection and Quantification: A Deep Learning Approach Using Multi-View Echocardiography.

Abstract

Background: Accurate evaluation of aortic regurgitation (AR) severity is necessary for early detection and chronic disease management. AR is most commonly assessed by Doppler echocardiography, however limitations remain given variable image quality and need to integrate information from multiple views. This study developed and validated a deep learning model for automated AR severity assessment from multi-view color Doppler videos.

Methods: We developed a video-based convolutional neural network (R2+1D) to classify AR severity using color Doppler echocardiography videos from five standard views: parasternal long-axis (PLAX), PLAX-aortic valve focus, apical three-chamber (A3C), A3C-aortic valve focus, and apical five-chamber (A5C). The model was trained on 47,638 videos from 32,396 studies (23,240 unique patients) from Cedars-Sinai Medical Center (CSMC) and externally validated on 3369 videos from 1504 studies (1493 unique patients) from Stanford Healthcare Center (SHC).

Results: Combining assessments from multiple views, the EchoNet-AR model achieved excellent identification of both at least moderate AR (AUC 0.95, [95% CI 0.94-0.96]) and severe AR (AUC 0.97, [95% CI 0.96 - 0.98]). This performance was consistent in the external SHC validation cohort for both at least moderate AR (AUC 0.92, [95% CI 0.88-0.96]) and severe AR (AUC 0.94, [95% CI 0.89-0.98]). Subgroup analysis showed robust model performance across varying image quality, valve morphologies, and patient demographics. Saliency map visualizations demonstrated that the model focused on the proximal flow convergence zone and vena contracta, appropriately narrowing on hemodynamically significant regions.

Conclusion: The EchoNet-AR model accurately classifies AR severity and synthesizes information across multiple echocardiographic views with robust generalizability in an external cohort. The model shows potential as an automated clinical decision support tool for AR assessment, however clinical interpretation remains essential, particularly in complex cases with multiple valve pathologies or altered hemodynamics.