Artificial Intelligence in Coronary Plaque Characterization: Clinical Implications, Evidence Gaps, and Future Directions

TL;DR

AI improves coronary plaque analysis for heart disease, but challenges like data bias and integration remain.

Contribution

Highlights AI's role in coronary plaque analysis and identifies barriers to clinical adoption.

Findings

AI models show high accuracy in detecting and analyzing coronary plaque features.

Combining AI biomarkers with clinical scores improves cardiovascular risk prediction.

Barriers to AI adoption include data heterogeneity, bias, and regulatory issues.

Abstract

Coronary artery disease (CAD) remains the leading cause of cardiovascular morbidity and mortality worldwide, with plaque composition and morphology being as key determinants of disease progression and clinical outcomes. Accurate plaque characterization is essential for risk stratification and therapeutic decision-making, yet conventional image interpretation is limited by inter-observer variability and time-intensive workflows. Artificial intelligence (AI) models have emerged as a transformative tool for automated coronary plaque analysis across multiple imaging modalities. AI-driven models demonstrate high diagnostic accuracy for plaque detection, segmentation, quantification, and vulnerability assessment. Integration of AI-derived imaging biomarkers with clinical risk scores can further enhance prediction of major adverse cardiovascular events and supports personalized management. These advances position AI-enhanced imaging as a powerful adjunct for both invasive and non-invasive evaluation of CAD. Despite its promise, important barriers to widespread clinical adoption remain, including data heterogeneity, algorithmic bias, limited model transparency, insufficient prospective validation, regulatory challenges, and incomplete integration into clinical workflows. Addressing these challenges will be essential to ensure safe, generalizable, and cost-effective implementation of AI in routine cardiovascular care.