Fully automated artificial intelligence-based coronary CT angiography image processing: efficiency, diagnostic capability, and risk stratification.

Journal: European radiology
Published Date: Jan 9, 2024

Abstract

OBJECTIVES: To prospectively investigate whether fully automated artificial intelligence (FAAI)-based coronary CT angiography (CCTA) image processing is non-inferior to semi-automated mode in efficiency, diagnostic ability, and risk stratification of coronary artery disease (CAD).

Authors

  • Yaping Zhang
    Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080 China.
  • Yan Feng
    Clinical Pharmacology and Pharmacometrics, Bristol-Myers Squibb, Lawrenceville, New Jersey, USA.
  • Jianqing Sun
    Philips Healthcare, Shanghai 510530, China.
  • Lu Zhang
    Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States.
  • Zhenhong Ding
    Shukun (Beijing) Technology Co, Ltd, Jinhui Bd, Qiyang Rd, Beijing, 100102, China.
  • Lingyun Wang
    Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, China. wangly@xmu.edu.cn.
  • Keke Zhao
    Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Zhijie Pan
    Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China.
  • Qingyao Li
    Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080, China.
  • Ning Guo
  • Xueqian Xie
    Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai, 200080 China.

Keywords

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