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 4-chamber B-mode echocardiographic cine loops from 11,244 studies across 20 US states. The model was designed to differentiate clinically significant (moderate/severe) from nonsignificant (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 3 echocardiographer independently regraded all external validation studies to assess interobserver variability.

RESULTS

On external validation, MitralVision achieved an area under the receiver operating characteristic curve (AUROC) of 0.91, sensitivity 82.1%, specificity 84.3%, negative predictive value 91.6%, and positive predictive value 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 5 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 artificial intelligence-based approach offers reproducible MR assessment and may be compatible with future workflow implementation in high-throughput or resource-limited settings.