PROJECT SUMMARY From parent grant: Coronary artery disease remains the leading cause of death worldwide, and more than half of the individuals suffering myocardial infarction (heart attacks) have no premonitory symptoms. Studies of patients with coronary artery disease have traditionally focused only on the severity of narrowing (stenosis) of the coronary arteries by atherosclerotic plaques, rather than the adverse features of coronary plaques which are predisposed to rupture and precipitate myocardial infarction. Coronary CT Angiography (CTA) is a noninvasive test that allows assessment of both coronary stenosis and plaque characteristics. Currently, however, CTA is interpreted visually for stenosis. Quantitative measurements of CTA stenosis severity and plaque features are not part of current clinical routine. In this project we propose to automate, using artificial intelligence (AI), the measurement of coronary plaque characteristics and inflammation for patients undergoing coronary CT Angiography (CTA), a first-line noninvasive diagnostic test for chest pain. We further propose to accurately predict future major adverse cardiac events (such as heart attack), by integrating clinical data, CTA-measured coronary plaque and inflammation, using AI, in prospective trials and multicenter CTA registries. We propose three specific aims: 1) To refine, expand and automate measurements of coronary plaque and lumen for the entire coronary artery tree, and to standardize measurement of plaque changes in serial CTA; 2) To evaluate the prognostic value of automatically-quantified plaque features and PCAT characteristics for the prediction of future MACE in the prospective SCOT-HEART trial and multicenter CTA registries; 3) To develop and evaluate with full external validation a new automated patient risk score—combining patient clinical data, CTA-measured quantitative plaque features and PCAT characteristics, using machine learning— for the prediction of future MACE events in the prospective SCOT-HEART trial and multicenter CTA registries. The proposed work will enable automated, multi-faceted and reproducible analysis of plaque, stenosis and PCAT from CTA, combined with objective risk scores reflecting likelihood of adverse cardiovascular events. This work will provide a novel, personalized, real-world paradigm that objectively and accurately identifies individual patients at risk of future cardiovascular events, from routine CTA imaging. Additionally: In this research supplement, we propose 1) to compare quantitative plaque characteristics among different ethnic groups in asymptomatic patients from a prospective multi-ethnic registry, and 2) to establish reference ranges for coronary plaque measurements from all asymptomatic patients in Aim 1, with prognostic validation in prospective studies.