Deep learning-based fully automatic Risser stage assessment model using abdominal radiographs.

Journal: Pediatric radiology
Published Date:

Abstract

BACKGROUND: Artificial intelligence has been increasingly used in medical imaging and has demonstrated expert level performance in image classification tasks.

Authors

  • Jae-Yeon Hwang
    Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea.
  • Yisak Kim
    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.).
  • Jisun Hwang
    Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea.
  • Yehyun Suh
    Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
  • Sook Min Hwang
    Department of Radiology, Hallym University Kangnam Sacred Heart Hospital, Seoul, Republic of Korea.
  • Hyeyun Lee
    Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, College of Medicine, Pusan National University, Yangsan, Republic of Korea.
  • Minsu Park
    School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea.