Ruling out rotator cuff tear in shoulder radiograph series using deep learning: redefining the role of conventional radiograph.

Journal: European radiology
Published Date: Feb 5, 2020

Abstract

OBJECTIVE: To develop a deep learning algorithm that can rule out significant rotator cuff tear based on conventional shoulder radiographs in patients suspected of rotator cuff tear.

Authors

  • Youngjune Kim
    Department of Radiology, Seoul National University Bundang Hospital, Seongnam.
  • Dongjun Choi
    Department of Radiology, Seoul National University Bundang Hospital, Seongnam.
  • Kyong Joon Lee
    Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Republic of Korea.
  • Yusuhn Kang
    Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea. yskang0114@gmail.com.
  • Joong Mo Ahn
    Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea.
  • Eugene Lee
    Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea.
  • Joon Woo Lee
    Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea.
  • Heung Sik Kang
    Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea.

Keywords

External Resources

Popular Topics
Recent Journals