Accuracy of machine learning models for mitral regurgitation severity assessment: A systematic review and meta-analysis.

Background: Accurate assessment of mitral regurgitation (MR) severity is crucial for guiding clinical management, but is often limited by the subjectivity and variability of traditional echocardiographic evaluations. Machine learning (ML) models offer potential for automated, objective MR grading, yet their diagnostic performance remains underexplored. This systematic review and meta-analysis aim to evaluate the diagnostic accuracy of ML-based models for assessing MR severity. Methods: We searched five different databases for studies evaluating ML algorithms (deep learning or traditional ML) for MR severity assessment in adults. Data were extracted and the risk of bias was assessed using the PROBAST + AI tool. A bivariate random-effects model was used to pool diagnostic metrics, with heterogeneity quantified via I Results: Nine studies met inclusion criteria, demonstrating strong ML performance with a pooled AUROC of 0.97 (95% CI: 0.96-0.98), sensitivity of 0.93 (95% CI: 0.83-0.97), and specificity of 0.96 (95% CI: 0.92-0.98). High heterogeneity (I Conclusion: ML models demonstrate good diagnostic accuracy for assessing MR severity, with the potential to enhance clinical decision-making by reducing subjectivity. However, high heterogeneity and limited external validation necessitate prospective, standardized trials to ensure generalizability and clinical adoption.