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

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

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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 extrac-tion model with reduced order 3 dimentional(3D)computational fluid dynamics.A total of 463 consec-utive 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 evalu-ation.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.

Artificial intelligenceCoronary CTACT-derived fractional flow reserveFully-automatedOutcomes

Bangjun Guo、Mengchun Jiang、Xiang Guo、Chunxiang Tang、Jian Zhong、Mengjie Lu、Chunyu Liu、Xiaolei Zhang、Hongyan Qiao、Fan Zhou、Pengpeng Xu、Yi Xue、Minwen Zheng、Yang Hou、Yining Wang、Jiayin Zhang、Bo Zhang、Daimin Zhang、Lei Xu、Xiuhua Hu、Changsheng Zhou、Jianhua Li、Zhiwen Yang、Xinsheng Mao、Guangming Lu、Longjiang Zhang

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Department of Radiology,Jinling Hospital,Affiliated Hospital of Medical School,Nanjing University,Nanjing 210002,China

Department of Radiology,Affiliated Hospital of Jining Medical University,Jining 272007,China

Department of Radiology,Xijing Hospital,Fourth Military Medical University,Xi'an 733399,China

Department of Radiology,Shengjing Hospital of China Medical University,Shenyang 110022,China

Department of Radiology,Peking Union Medical College Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing 100005,China

Institute of Diagnostic and Interventional Radiology,and Department of Cardiology,Shanghai Jiao Tong University Affiliated Sixth People's Hospital,Shanghai 200235,China

Department of Radiology,Jiangsu Taizhou People's Hospital,Taizhou 225399,China

Department of Cardiology,Nanjing First Hospital,Nanjing Medical University,Nanjing 210012,China

Department of Radiology,Beijing Anzhen Hospital,Capital Medical University,Beijing 100029,China

Department of Radiology,Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,Hangzhou 310020,China

Department of Cardiology,Jinling Hospital,Affiliated Hospital of Medical School,Nanjing University,Nanjing 210002,China

Shukun(Beijing)Network Technology Co.,Ltd.,Beijing 102200,China

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国家重点研发计划Jiangsu Province Key Project of Comprehensive Prevention and Control of Chronic DiseasesTop Talent Support Program for young and middleaged people of Wuxi Health Committee

2022YFC2010004BE2020699BJ2023044

2024

10.1016/j.scib.2024.03.053

科学通报(英文版)
中国科学院

科学通报(英文版)

CSTPCD
ISSN：1001-6538
年,卷(期)：2024.69(10)
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