Advancing coronary artery disease risk stratification: integrating hyperspectral imaging with machine learning for precision cardiology

Coronary artery disease (CAD) remains one of the leading causes of death worldwide, making precise risk stratification essential for personalized medicine. Hyperspectral imaging (HSI) has emerged as a novel noninvasive medical imaging technology with significant potential in CAD detection and risk stratification. Previous reviews focusing on machine learning (ML) in CAD have predominantly centered on traditional imaging modalities or broadly explored the applications of ML in CAD. By exclusively focusing on the synergistic potential of HSI and ML, this review aims to advance CAD diagnosis and risk stratification. It systematically summarizes the current application, core technical advantages, and unresolved challenges of HSI in CAD diagnosis. By integrating HSI with ML, we propose a novel noninvasive diagnostic framework capable of extracting CAD-related biomarkers from superficial body features. Although challenges remain in data standardization, model generalizability, and clinical translation, this integrated approach holds great promise for advancing precision cardiology.