Artificial Intelligence in Valvular Heart Disease: Imaging, Quantification, Risk Prediction, and Potential Clinical Decision Support

Valvular heart disease (VHD) affects a substantial proportion of the population and remains a major cause of cardiovascular morbidity and mortality worldwide. Despite advances in surgical and transcatheter therapies, important challenges persist. VHD remains underdiagnosed, assessment of disease severity is often variable, and the optimal timing of intervention continues to evolve. Artificial intelligence (AI) has emerged as a promising tool across the spectrum of VHD care. AI-based electrocardiogram analysis can identify both existing and future VHD, while AI-guided point-of-care ultrasound extends screening to nonspecialist settings. Across multimodality imaging, machine learning (ML) and deep learning algorithms enable automated, reproducible assessment of VHD. In echocardiography, AI supports grading of disease severity across the main valvular lesions, including Doppler interpretation and automated report generation. AI-enhanced cardiac magnetic resonance facilitates automated assessment of ventricular remodeling and myocardial fibrosis, while AI-assisted cardiac computed tomography improves procedural planning for transcatheter interventions. In parallel, ML has identified distinct patient phenotypes in multiple valvular lesions with differing clinical trajectories and responses to intervention. This review summarizes current applications of AI in detection, multimodality imaging, and potential clinical decision support for VHD and discusses opportunities and challenges for integration into routine practice.