Artificial Intelligence in Hypertrophic Cardiomyopathy: Advances, Challenges, and Future Directions for Personalized...

Area of Science:

• Cardiovascular Medicine
• Biomedical Engineering
• Medical Informatics

Background:

• Hypertrophic cardiomyopathy (HCM) is a genetic heart condition with inadequate current risk stratification for sudden cardiac death.
• Existing methods for predicting clinical outcomes in HCM patients have limited accuracy.
• There is a critical need for advanced tools to personalize risk prediction and management in HCM.

Purpose of the Study:

• To review the validated clinical applications of artificial intelligence (AI) in transforming personalized risk prediction for HCM.
• To examine how AI is enhancing the management and treatment strategies for hypertrophic cardiomyopathy.
• To identify current challenges and future directions for AI implementation in HCM care.

Main Methods:

• Comprehensive literature search of PubMed, IEEE Xplore, Web of Science, and Scopus (January 2015 - January 2025).
• Inclusion of peer-reviewed studies on AI applications in HCM with validated performance metrics.
• Analysis of AI techniques, clinical applications, performance, and implementation barriers.

Main Results:

• Machine learning models show high accuracy (83%) in predicting ventricular arrhythmias, identifying novel predictors.
• Deep learning analysis of ECGs achieves 85-87% accuracy in sudden cardiac death prediction, surpassing traditional scores.
• AI enhances genetic testing (96% accuracy) and cardiac MRI analysis, with real-time screening and therapy decision support tools showing >90% accuracy.

Conclusions:

• Artificial intelligence offers transformative potential for improving risk prediction and management in hypertrophic cardiomyopathy.
• Validated AI applications in ECG, genetics, and imaging are advancing personalized HCM care.
• Addressing data bias, standardization, regulatory, and interpretability challenges is crucial for widespread clinical integration.