Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD

• Published in: Science bulletin Vol. 69; no. 10; pp. 1472 - 1485
• Main Authors: Guo, Bangjun, Jiang, Mengchun, Guo, Xiang, Tang, Chunxiang, Zhong, Jian, Lu, Mengjie, Liu, Chunyu, Zhang, Xiaolei, Qiao, Hongyan, Zhou, Fan, Xu, Pengpeng, Xue, Yi, Zheng, Minwen, Hou, Yang, Wang, Yining, Zhang, Jiayin, Zhang, Bo, Zhang, Daimin, Xu, Lei, Hu, Xiuhua, Zhou, Changsheng, Li, Jianhua, Yang, Zhiwen, Mao, Xinsheng, Lu, Guangming, Zhang, Longjiang
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 30.05.2024
• Subjects: Aged; Artificial Intelligence; Computed Tomography Angiography - methods; Coronary Angiography - methods; Coronary Artery Disease - diagnosis; Coronary Artery Disease - diagnostic imaging; Coronary Artery Disease - physiopathology; Coronary CTA; CT-derived fractional flow reserve; Female; Fractional Flow Reserve, Myocardial - physiology; Fully-automated; Humans; Male; Middle Aged; Outcomes; Prognosis; Retrospective Studies; Coronary CTA; Fully-automated; Artificial intelligence; Outcomes; CT-derived fractional flow reserve
• ISSN: 2095-9273; 2095-9281
• DOI: 10.1016/j.scib.2024.03.053

[Display omitted] Currently, clinically available coronary CT angiography (CCTA) derived fractional flow reserve (CT-FFR) is time-consuming and complex. We propose a novel artificial intelligence-based fully-automated, on-site CT-FFR technology, which combines the automated coronary plaque segmentation and luminal extraction model with reduced order 3 dimentional (3D) computational fluid dynamics. A total of 463 consecutive patients with 600 vessels from the updated China CT-FFR study in Cohort 1 undergoing both CCTA and invasive fractional flow reserve (FFR) within 90 d were collected for diagnostic performance evaluation. For Cohort 2, a total of 901 chronic coronary syndromes patients with index CT-FFR and clinical outcomes at 3-year follow-up were retrospectively analyzed. In Cohort 3, the association between index CT-FFR from triple-rule-out CTA and major adverse cardiac events in patients with acute chest pain from the emergency department was further evaluated. The diagnostic accuracy of this CT-FFR in Cohort 1 was 0.82 with an area under the curve of 0.82 on a per-patient level. Compared with the manually dependent CT-FFR techniques, the operation time of this technique was substantially shortened by 3 times and the number of clicks from about 60 to 1. This CT-FFR technique has a highly successful (> 99%) calculation rate and also provides superior prediction value for major adverse cardiac events than CCTA alone both in patients with chronic coronary syndromes and acute chest pain. Thus, the novel artificial intelligence-based fully automated, on-site CT-FFR technique can function as an objective and convenient tool for coronary stenosis functional evaluation in the real-world clinical setting.