Advances in artificial intelligence for the evaluation of mitral regurgitation

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Abstract
Purpose of review: The diagnostic evaluation of mitral regurgitation (MR) is complex, time-intensive, and prone to significant interobserver variability. This review examines the current evidence on artificial intelligence (AI) applications across the full MR diagnostic pathway, from pre-imaging screening to advanced multimodality imaging, and explores future directions for clinical integration. Recent findings: AI-enabled digital stethoscopes and deep learning-based electrocardiographic models provide scalable upstream strategies for early detection and population-level risk stratification, although they currently function as enrichment tools rather than standalone diagnostics. In echocardiography, AI has demonstrated strong performance for automated valve segmentation, Doppler analysis, severity grading, and phenotypic classification. In cardiac magnetic resonance, AI enables automated valve tracking, ventricular segmentation, and tissue characterization, although dedicated algorithms for direct MR quantification remain under development. Beyond automation, AI-driven approaches have identified clinically meaningful phenotypes linking valvular dysfunction and cardiac remodeling with myocardial fibrosis and increased cardiovascular risk. Summary: AI holds significant promise to improve reproducibility, consistency, and clinical integration of MR assessment. Widespread implementation, however, requires prospective validation, standardized acquisition protocols, improved model interpretability and, most importantly, proper regulations driven by a culture of safety.

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