Comparison and verification of two deep learning models for the detection of chest CT rib fractures.

Journal: Acta radiologica (Stockholm, Sweden : 1987)
Published Date: Mar 18, 2022

Abstract

BACKGROUND: A high false-positive rate remains a technical glitch hindering the broad spectrum of application of deep-learning-based diagnostic tools in routine radiological practice from assisting in diagnosing rib fractures.

Authors

  • Sun Hongbiao
    Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China.
  • Xu Shaochun
    Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China.
  • Wang Xiang
    Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems(No.2017TP1016), Changsha University of Science and Technology, Changsha, Hunan, China.
  • Tang YuRun
    Company 13, College of Basic Medical Sciences, Naval Medical University, Shanghai, PR China.
  • Lu Yang
    Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Zhang Mingzi
    Shanghai Aitrox Technology Corporation Limited, Shanghai, PR China.
  • Yang Hua
    Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Zhao Keyang
    Shanghai Aitrox Technology Corporation Limited, Shanghai, PR China.
  • Fu Chi-Cheng
    Shanghai Aitrox Technology Corporation Limited, Shanghai, PR China.
  • Fang Qu
    Shanghai Aitrox Technology Corporation Limited, Shanghai, PR China.
  • Gu Pengchen
    Shanghai Aitrox Technology Corporation Limited, Shanghai, PR China.
  • Xiao Yi
    Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China.
  • Liu Shiyuan
    Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, PR China.

Keywords

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