Multimodal Machine Learning Approaches for Enhanced Detection and Management of Hypertrophic Cardiomyopathy

This study addresses the shortcomings in diagnosing and managing HCM (Hypertrophic Cardiomyopathy), a genetic heart disease affecting 0.5-1.5% of the global population, by employing advanced ML (Machine Learning) approaches. The research integrates multimodal data, including cardiac MRI, genetic, clinical, and outcome information, to design and implement interpretable ML models capable of diagnosing HCM, assessing disease progression, and indicating individualized treatment courses. To enhance generalization and facilitate clinical application, a large multi-center dataset is utilized. Results demonstrate that the developed ML models achieve higher accuracy in HCM diagnosis and risk stratification compared to traditional methods, with particular strength in early detection and prognosis prediction. This work not only aims to improve care for HCM patients but also establishes a foundation for applying ML algorithms to other complex genetic cardiac diseases, potentially shifting the paradigm of cardiovascular medicine towards a more targeted approach. The findings suggest significant potential for improving patient outcomes through personalized treatment strategies and more accurate risk evaluation, setting the stage for further research into ML applications in complex cardiovascular disorders and potentially revolutionizing precision medicine in cardiology.