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.

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.