Deep learning powered coronary CT angiography for detecting obstructive coronary artery disease: The effect of reader experience, calcification and image quality.

Journal: European journal of radiology
Published Date: Jun 27, 2021

Abstract

OBJECTIVES: To investigate the effect of reader experience, calcification and image quality on the performance of deep learning (DL) powered coronary CT angiography (CCTA) in automatically detecting obstructive coronary artery disease (CAD) with invasive coronary angiography (ICA) as reference standard.

Authors

  • Chun Yu Liu
    Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China.
  • Chun Xiang Tang
  • Xiao Lei Zhang
    Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
  • Sui Chen
    Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China.
  • Yuan Xie
  • Xin Yuan Zhang
    Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, PR China.
  • Hong Yan Qiao
    Department of Medical Imaging, Jinling Hospital, Nanjing Medical University, Nanjing, 210002, Jiangsu, China.
  • Chang Sheng Zhou
  • Peng Peng Xu
    Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
  • Meng Jie Lu
  • Jian Hua Li
    Department of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
  • Guang Ming Lu
  • Long Jiang Zhang

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