人工智能在冠状动脉CT血管成像后处理和诊断报告的初步评估

目的 探讨人工智能（AI）在冠状动脉CT血管造影（CCTA）的图像后处理和诊断报告中的应用价值。 方法 选取重庆医科大学附属第三医院于2019年4月至7月就诊的64例疑似冠心病患者，其中男性40例、女性24例，年龄（62.16±14.13）岁。所有患者均行CCTA扫描，按照李克特量表评分标准对原始图像质量进行评分，分别进行人工和AI图像后处理，比较二者的用时及合格率、诊断报告的用时及对冠状动脉斑块的诊断效能。 结果 CCTA扫描后，冠状动脉AI图像后处理的时间约3 min，合格率为92.2%（59/64）；人工后处理的时间为20~30 min。与人工处理相比，冠状动脉AI后处理的图像中冠状动脉管壁更光滑、小分支显示更全面、血管对比更清晰，并且能自动识别冠状动脉狭窄。冠状动脉AI图像的诊断报告在图像重建后即可完成（<1 min），而人工的诊断报告需15 min左右才能完成。冠状动脉AI与人工对冠状动脉斑块检出的灵敏度几乎一致，分别为93.3%和92.0%；人工诊断报告对斑块检出的特异度达100%，而AI的特异度为93.8%。 结论 冠状动脉AI在图像后处理速度、图像质量及报告诊断的效率方面具有一定优势，有望成为CCTA分析的有效辅助工具。

Artificial intelligence in coronary CT angiography post-processing and preliminary evaluation of diagnostic reports

Objective To explore the value of coronary artificial intelligence (AI) in the post-processing and diagnosis of coronary CT angiography (CCTA). Methods Sixty-four patients with suspected coronary heart disease who were admitted to Third Affiliated Hospital of Chongqing Medical University from April to July 2019, including 40 males and 24 females, aged (62.16±14.13) years, were randomly selected. All patients underwent coronary CT angiography. The original image quality was scored in accordance with the Likert scoring standard, and artificial and AI image post-processing were carried out. The time, qualified rate, time of the diagnosis report, and diagnostic efficiency of the two were compared. Results The post-processing time of AI images of the coronary arteries was about 3 min, and the time of artificial post-processing was 20?30 min after CCTA. The qualified rate of AI post-processing of the coronary arteries was 92.2% (59/64). Compared with manual processing, the AI images of the coronary arteries after processing were smoother, had more small branches and clearer vessel contrast, and can automatically identify coronary artery stenosis. The diagnosis report of coronary artery AI images was completed immediately after image reconstruction (< 1 min), whereas the artificial diagnosis report was about 15 min. The sensitivity of AI plaque in the coronary artery was almost the same as that of artificial detection (i.e., 93.3% and 92.0%, respectively). The specificity of the artificial diagnosis report was 100% and that of AI was 93.8%. Conclusion Coronary AI has certain advantages in image post-processing speed, image quality and efficiency of reporting diagnosis, and is expected to be an effective auxiliary tool for CCTA analysis.