人工智能自动重建用于评估冠状动脉旁路移植物  

Artificial intelligence automatic reconstruction for evaluating coronary artery bypass graft

作　　者：唐瑞遥 张树桐[1] 黄增发 刘妮 丁义 杜昕雨 王翔 TANG Ruiyao;ZHANG Shutong;HUANG Zengfa;LIU Ni;DING Yi;DU Xinyu;WANG Xiang(Department of Radiology,the Central Hospital of Wuhan,Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430014,China)

机构地区：[1]华中科技大学同济医学院附属武汉中心医院影像诊断科,湖北武汉430014

出　　处：《中国介入影像与治疗学》2025年第1期27-31,共5页Chinese Journal of Interventional Imaging and Therapy

摘　　要：目的 评价基于深度学习(DL)的人工智能(AI)自动重建用于评估冠状动脉旁路移植术(CABG)后移植物的价值。方法 回顾性分析90例共存在197具移植物的CABG后患者的冠状动脉CT血管成像图,以人工评价结果(人工组)为标准,评估AI(AI组)判断移植物及远端自体血管狭窄程度的效能;比较组间计算未受保护冠状动脉区域(UCT)的一致性,以及图像后处理及诊断总耗时。结果 AI组判断移植物数量与人工组的一致性一般[组内相关系数(ICC)=0.743,P<0.05],其判断移植物最大狭窄程度的一致性为一般至优(Kappa为0.310~1.000,P均<0.05),判断移植物远端自体血管最大狭窄程度的一致性为一般至良好(Kappa为0.292~0.795,P均<0.05)。AI组计算UCT结果与人工组具有中等一致性(ICC=0.469,P<0.05)。AI组诊断UCT的曲线下面积为0.811。AI组图像后处理及诊断总耗时较人工组为短(P<0.05)。结论 AI用于自动重建冠状动脉旁路移植物、量化移植物狭窄程度效率较高,且与人工评估结果的一致性尚可,可作为辅助手段。Objective To evaluate the value of deep learning(DL)-based artificial intelligence(AI)automatic reconstruction for evaluation of grafts in patients who underwent coronary artery bypass grafting(CABG).Methods Coronary CT angiography data of 90 patients who underwent CABG with a total of 197 grafts were retrospectively analyzed.Taken manual evaluation results(manual group)as the standards,the efficacy of AI(AI group)for evaluating the degree of stenosis of graft and distal autologous blood vessels were assessed.The consistency between calculating unprotected coronary territory(UCT)and the total time for image post-processing and diagnosis were compared between groups.Results AI group showed average consistency with manual group for evaluating the number of grafts([intra-class correlation coefficient,ICC]=0.743,P<0.05),average to excellent for evaluating the maximum degree of graft stenosis(Kappa=0.310—1.000,all P<0.05),also average to good consistency for evaluating the maximum degree of stenosis of the native vessel distal to the graft insertion(Kappa=0.292—0.795,all P<0.05).AI group had moderate consistency with manual group for UCT(ICC=0.469,P<0.05),achieved an area under the curve of 0.811.The overall time of image post-processing and diagnosis in AI group were both significantly shorter than that in manual group(P<0.05).Conclusion Having acceptable consistency with manual evaluation and ability for assistant,AI was efficient for automatic reconstructing coronary artery bypass graft and quantifying the degree of graft stenosis.

关 键 词：冠状动脉疾病 体层摄影术 X线计算机 血管造影术 人工智能 冠状动脉分流术 

分 类 号：R543.3[医药卫生—心血管疾病] R817.42[医药卫生—内科学]