摘要
Abstract
Objective To investigate the correlation between coronary artery calcium score(CACS)measured by coronary computed tomography(CCTA)combined with the Sukun Technology Intelligence Platform(AI),CT fractional flow reserve(CT-FFR),and plaque characteristics.Methods Based on the CACS values measured by AI,208 patients who underwent CCTA examination at the First Affiliated Hospital of Bengbu Medical College from January 2021 to December 2022 were divided into three groups:low calcification group(n=73):0<CACS<100;moderate calcification group(n=64):100≤CACS≤400,high calcification group(n=71):CACS>400.Comparison of general clinical data and characteristics of culprit vessels and culprit plaques measured by AI in different CACS groups were analyzed.The correlation between characteristic parameters and CACS groups was assessed,and the diagnostic efficiency of single and combined indicators for two groups(low calcification group vs.moderate calcification group,moderate calcification group vs.high calcification group)was evaluated by drawing ROC curves to calculate the AUC.Results Based on AI measurements,there were statistically significant differences(P<0.05)in CT-FFR,plaque length,plaque volume,and minimal lumen area(MLA)among different CACS groups.There was a significant difference in plaque type between the low calcification group and the moderate calcification group(P<0.05),but no statistical significance in differences between the moderate calcification group and the high calcification group(P>0.05).Multifactorial logistic regression analysis indicated that age,CT-FFR,plaque volume,and MLA were risk factors for higher CACS groups.Plaque volume was positively correlated with the severity of CACS(r=0.437,P<0.001),while CT-FFR and MLA were negatively correlated with it(r=-0.640,-0.658,P<0.001).The ROC curve showed that in the low calcification group to the moderate calcification group,the AUC values of CT-FFR,plaque volume,MLA and combined index are 0.731,0.678,0.748 and 0.824 respectively;in the moderate calcification group to the high calcification group,the AUC values of CT-FFR,plaque volume,MLA and combined index were 0.741,0.670,0.746 and 0.840 respectively.The diagnostic efficiency of the combined index of CT-FFR,plaque volume and MLA was greater than that of a single index in both groups.Conclusion AI measurements show significant differences in CT-FFR,plaque volume,and MLA among different CACS groups,indicating that they are risk factors for higher CACS groups.CT-FFR and MLA demonstrate good diagnostic performance across different CACS groups,while the combination of CT-FFR,plaque volume,and MLA significantly improves diagnostic efficacy.关键词
人工智能/冠状动脉钙化积分/无创血流储备分数/斑块特征Key words
artificial intelligence/coronary artery calcification score/CT fractional flow reserve/plaque characteristics
