Objective To investigate the diagnostic value of non-invasive fractional flow reserve(CTFFR)based on deep learning combined with the quantitative analysis of plaque components in coronary computed tomography angiography(CCTA)for coronary heart disease.Method A retrospective collection of 22 cases each of acute coronary syndrome(ACS)and chronic coronary syndrome(CCS)was conducted.The coronary plaque components and lesion segment FFRCT values were analyzed.Results The volume and percentage of fibrous plaque(FPV,FPV%),the volume and percentage of lipid-rich plaque(LPV,LPV%),the total plaque volume and percentage(TPV,TPV%),as well as the flow reserve difference(ΔFFRCT)in the ACS group were significantly higher than those in the CCS group,with the vessel/plaque volume ratio(L/P)and FFRCT values being lower in the ACS group(P<0.001).The combined diagnosis of FFRCT,ΔFFRCT,L/P,TPV%,and TPV in the ACS group for AUC was superior to a single indicator(P<0.001).Conclusion The combination of FFRCT and CCTA plaque characteristics offers certain discriminative power between ACS and CCS,providing clinical risk stratification for coronary heart disease.
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