Abstract：Objective： To analyze the value of artificial intelligence (AI) for coronary CT angiography (CCTA) image postprocessing and diagnosis of coronary artery disease. Methods： The imaging data of 40 patients who both underwent CCTA and percutaneous coronary arteriography (CAG) from January 2020 to January 2022 were retrospectively collected and analyzed. Image postprocessing and diagnosis were divided into manual group and AI group to compare the differences in postprocessing time and subjective image quality scores and to assess the differences in coronary plaque properties. Using CAG results as the gold standard, the differences in sensitivity, specificity, positive predictive value, negative predictive value and accuracy in the diagnosis of coronary artery stenosis between the two were compared on a vessel-by-vessel basis, and the consistency of the results was evaluated using the Kappa test. Results： The postprocessing and diagnosis time in the AI group was (236.57±20.66)s, which was approximately 70.04% shorter than that in the manual group (789.74±63.38)s. The difference was statistically significant (P<0.05); The difference between the objective and subjective scores of image quality obtained by the two methods was not statistically significant (all P>0.05). The overall accuracy of plaque detection in the AI group was 96.32% (131/136). The differences in the detection of calcified coronary plaque, noncalcified plaque and mixed plaque between the manual and AI groups were not statistically significant (P>0.05) and were in good agreement (Kappa=0.901, P<0.001). On a vesselbyvessel basis, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the AI group in diagnosing coronary artery stenosis were 87.72% (50/57), 94.12% (48/51), 94.34% (50/53), 87.27% (50/53) and 90.74% (98/108), respectively, and the CAG in diagnosing coronary artery stenosis with good consistency (Kappa=0.815, P<0.001). Conclusion： AI has certain advantages in CCTA image post-processing efficiency, plaque nature identification and coronary stenosis, and can be used as an effective aid for physicians to analyze and diagnose coronary artery disease.
收稿日期: 2022-06-13
作者简介: 祁冬（1989—），男，主管技师，主要从事影像技术、血管三维后处理及影像组学研究。
引用本文:
祁冬， 姚传顺， 胡淑敏， 何兴义， 姚木子， 沈朝军. 人工智能在冠状动脉CT血管成像图像后处理和冠状动脉狭窄诊断中的应用[J]. 江苏大学学报:医学版, 2023, 33(04): 323-327,332.
QI Dong, YAO Chuanshun, HU Shumin, HE Xingyi, YAO Muzi, SHEN Chaojun. The value of artificial intelligence in post-processing coronary CTA images and diagnosing coronary artery stenosis. Journal of Jiangsu University(Medicine Edition), 2023, 33(04): 323-327,332.
