Abstract
Invasive methods are traditionally used for the identification of vulnerable plaques (VP) in coronary arteries. Coronary computed tomography angiography (CCTA) has emerged as a reliable non-invasive surrogate to assess VP. However, VP assessment on CCTA is time-consuming and requires specialised training limiting its clinical translation. We aim to develop and test a fully automated deep learning (DL) system capable of accurate VP characterisation on CCTA.
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