Automated Detection of Clinically Significant Mitral Regurgitation from Single-View B-mode Echocardiography Using Deep Learning.

INTRODUCTION: Mitral regurgitation (MR) is one of the most prevalent valvular heart diseases, and its diagnosis traditionally relies on Doppler echocardiography, which is subject to significant variability and technical challenges. We developed and externally validated MitralVision, a deep learning model for automated classification of clinically significant MR using single-view, B-mode echocardiographic loops. METHODS: MitralVision, a deep neural network, was trained on 28,487 apical four-chamber (A4C) B-mode echocardiographic cine loops from 11,244 studies across 20 U.S. states. The model was designed to differentiate clinically significant (moderate/severe) from non-significant (none/trace/mild) MR using grayscale cine loops without Doppler input. External validation was performed on 629 studies from 26 independent clinical sites using the original clinical interpretation as the reference standard. A separate board-certified Level III echocardiographer independently re-graded all external validation studies to assess interobserver variability. RESULTS: On external validation, MitralVision achieved an AUROC of 0.91, sensitivity 82.1%, specificity 84.3%, negative predictive value 91.6%, and positive predictive value of 69.3%, compared with the original clinical read. Interobserver agreement between the original clinical read and the additional expert reader was 75.2% for clinically significant MR, with exact agreement across five MR grades of 35.5%. When benchmarking the additional expert reader's interpretation, model AUROC was 0.89. The model demonstrated excellent calibration (Brier score 0.12; expected calibration error 0.03). CONCLUSIONS: MitralVision reliably distinguishes clinically significant MR using single-view B-mode echocardiography without Doppler input for model inference and may support more standardized MR screening. This streamlined AI-based approach offers reproducible MR assessment and may be compatible with future workflow implementation in high-throughput or resource-limited settings.