Application of symmetry evaluation to deep learning algorithm in detection of mastoiditis on mastoid radiographs.

Journal: Scientific reports
Published Date:

Abstract

As many human organs exist in pairs or have symmetric appearance and loss of symmetry may indicate pathology, symmetry evaluation on medical images is very important and has been routinely performed in diagnosis of diseases and pretreatment evaluation. Therefore, applying symmetry evaluation function to deep learning algorithms in interpreting medical images is essential, especially for the organs that have significant inter-individual variation but bilateral symmetry in a person, such as mastoid air cells. In this study, we developed a deep learning algorithm to detect bilateral mastoid abnormalities simultaneously on mastoid anterior-posterior (AP) views with symmetry evaluation. The developed algorithm showed better diagnostic performance in diagnosing mastoiditis on mastoid AP views than the algorithm trained by single-side mastoid radiographs without symmetry evaluation and similar to superior diagnostic performance to head and neck radiologists. The results of this study show the possibility of evaluating symmetry in medical images with deep learning algorithms.

Authors

  • Dongjun Choi
    Department of Radiology, Seoul National University Bundang Hospital, Seongnam.
  • Leonard Sunwoo
    Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Republic of Korea.
  • Sung-Hye You
    From the Graduate School of Medical Science and Engineering, Korea Advanced Institute for Science and Technology (KAIST), Daejeon, Republic of Korea (K.S.C., B.J.); Department of Radiology, Korea University College of Medicine, Anam Hospital, Seoul, Republic of Korea (S.H.Y.); Bio Imaging and Signal Processing Laboratory, Department of Bio and Brain Engineering, Korea Advanced Institute for Science and Technology (KAIST), Daejeon, Republic of Korea (Y.H., J.C.Y.); Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul 110-744, Republic of Korea (S.H.C.); Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.H.C.); Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea (S.H.C.); KAIST Institute for Health Science and Technology, Korea Advanced Institute for Science and Technology (KAIST), Daejeon, Republic of Korea (B.J.); and KAIST Institute for Artificial Intelligence, Korea Advanced Institute for Science and Technology (KAIST), Daejeon, Republic of Korea (B.J.).
  • 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.
  • Inseon Ryoo
    Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea. isryoo@gmail.com.