Artificial Intelligence-Enhanced Cardiac Magnetic Resonance in Hypertrophic Cardiomyopathy: Diagnosis, Phenotypic Characterization, and Risk Prediction

Hypertrophic cardiomyopathy (HCM) is a genetically and phenotypically heterogeneous myocardial disease associated with a broad spectrum of clinical outcomes, including heart failure, atrial fibrillation, ventricular arrhythmias, and sudden cardiac death. Cardiac magnetic resonance (CMR) plays a central role in the comprehensive assessment of HCM by enabling integrated evaluation of myocardial morphology, ventricular and atrial function, tissue characterization, myocardial deformation, and longitudinal disease progression within a single comprehensive imaging modality. However, conventional CMR interpretation remains limited by manual post-processing, interobserver variability, sequence-dependent measurements, and reliance on a relatively restricted set of predefined imaging parameters. These limitations may hinder the full exploitation of the rich multidimensional information contained in CMR datasets. Artificial intelligence (AI) approaches, particularly machine learning and deep learning, together with radiomics-based quantitative image analysis, provide new opportunities to extend the diagnostic and prognostic value of CMR to extract high-dimensional and quantitative information from cine imaging, late gadolinium enhancement, parametric mapping, and feature-tracking-based deformation analysis. Emerging evidence suggests that AI-enhanced CMR may facilitate automated image analysis, improve diagnostic classification, support differentiation of HCM from phenocopies, enable tissue- and deformation-based phenotypic characterization, and enhance individualized risk prediction. Although several studies have included independent or external testing, many remain retrospective, rely on selected populations or internal validation, and lack prospective assessment of clinical utility. High discrimination performance should not be interpreted as established clinical utility, because calibration, decision-curve analysis, clinically relevant thresholds, and prospective clinical impact remain insufficiently evaluated. In addition, interpretability and clinical generalizability remain important challenges for translation into routine practice. This review summarizes the current role of CMR in HCM and discusses emerging applications, methodological challenges, and future directions of AI-enhanced CMR for diagnosis, phenotypic characterization, and risk stratification.