Assessment of automatic rib fracture detection on chest CT using a deep learning algorithm.

Abstract

Authors

• Shuhao Wang
  Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, P. R. China.
• Dijia Wu
• Lifang Ye
  Department of Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai, 200233, China.
• Zirong Chen
  From the Departments of Radiology (M.L., LY., J.Z.) and Cardiology (W.Y.), Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, 85 Wujin Rd, Shanghai 200080, China; Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China (R.L.); and Shanghai United Imaging Intelligence, Shanghai, China (Z.C., D.W.).
• Yiqiang Zhan
• Yuehua Li
  From the Institute of Diagnostic and Interventional Radiology (Y.L., M.Y., X.D., J.Z.) and Department of Cardiology (Z.L., C.S.), Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, China 200233; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China (Y.W.); and Department of Radiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (B.L.).

Keywords

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