Deep learning for automated hip fracture detection and classification : achieving superior accuracy.

Abstract

Authors

• Zhiqian Zheng
  Department of Orthopedic Surgery, Seoul National University Hospital, Seoul, South Korea.
• Byeong Y Ryu
  Department of Orthopedic Surgery, Seoul National University Hospital, Seoul, South Korea.
• Sung E Kim
  Department of Orthopedic Surgery, Seoul National University Hospital, Seoul, South Korea.
• Dae S Song
  CONNECTEVE, Seoul, South Korea.
• Seong H Kim
  Department of Orthopedic Surgery, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, South Korea.
• Jung-Wee Park
  From the Interdisciplinary Program in Bioengineering (Y.K., Y.S.) and Integrated Major in Innovative Medical Science (Y.K.), Seoul National University Graduate School, Seoul, Republic of Korea; Department of Radiology (Y.K.), Transdisciplinary Department of Medicine & Advanced Technology (Y.G.K., B.W.K., Y.S.), and Department of Internal Medicine (J.H.K., C.S.S.), Seoul National University Hospital, Seoul, Republic of Korea; Departments of Orthopaedic Surgery (J.W.P., Y.K.L.) and Internal Medicine (S.H.K.), Seoul National University Bundang Hospital, 82 Gumi-ro 173 Beon-gil, Bundang gu, Seongnam, Republic of Korea; Departments of Medicine (Y.G.K.) and Internal Medicine (S.H.K., J.H.K., S.W.K., C.S.S.), Seoul National University College of Medicine, Seoul, Republic of Korea; and Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul, Republic of Korea (S.W.K.).
• Du H Ro
  Department of Orthopedic Surgery, Seoul National University Hospital, Seoul, South Korea.

Keywords

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