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Identifying various types of plaque, including calcified, non-calcified, and mixed, can furnish crucial insights into a patient's susceptibility to cardiovascular disease and aid in making treatment decisions. Manual quantification of coronary artery plaque on coronary CT angiography (CCTA) poses several challenges, including subjectivity, time consumption, and potential measurement errors.

Our objective was to develop a context-aware deep network (CADN) that could automatically evaluate coronary artery plaque in a sizable, multi-center prospective CCTA cohort.

The APOLLO study is a multi-center study involving CCTA scans of 5,000 Asian patients. At the present stage of this study, CCTA analysis was performed on a subset of 474 patients (138 females; mean age of 58 ± 11 years), of whom 76% were Chinese, 17% were …