Artificial Intelligence-based Coronary Plaque Quantification Using Coronary CT Angiography: Current Insights and...

Over the past decade, coronary CT angiography (CCTA) has seen major advancements in spatial and temporal resolution, as well as postprocessing software that now integrates artificial intelligence-based quantitative plaque (AI-QP) analysis tools. This review highlights the clinical significance of plaque quantification, examines the validation status of various AI-QP vendors, and discusses implications for patient care and outcomes. As plaque burden and progression are increasingly recognized as key prognostic indicators in patients with coronary artery disease (CAD), AI-QP has the potential to more accurately identify individuals at risk for adverse events and thereby influence patient management. However, several challenges must be addressed. The incremental prognostic value of AI-QP over established measures of plaque burden, such as the coronary artery calcium score, must be further established. For monitoring CAD progression, the reproducibility of AI-QP across CCTA scans must also be validated, and clinically meaningful thresholds for plaque progression need to be defined. Although AI-QP shows promise for improving risk stratification and guiding treatment, further evidence is needed to confirm its clinical utility and overcome barriers to widespread implementation in clinical practice. Keywords: CT-Coronary Angiography, Applications - CT, Artificial Intelligence © RSNA, 2025.