Effect of Deep Learning-Based Artificial Intelligence on Radiologists' Performance in Identifying Nigrosome 1 Abnormalities on Susceptibility Map-Weighted Imaging.

Journal: Korean journal of radiology
Published Date:

Abstract

OBJECTIVE: To evaluate the effect of deep learning (DL)-based artificial intelligence (AI) software on the diagnostic performance of radiologists with different experience levels in detecting nigrosome 1 (N1) abnormalities on susceptibility map-weighted imaging (SMwI).

Authors

  • Jiyeon Park
    Department of Radiation Oncology, University of Florida, Gainesville, FL, USA.
  • Chae Young Lim
    Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
  • So Yeon Won
    Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea.
  • Han Kyu Na
    Department of Neurology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Phil Hyu Lee
    Department of Neurology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Sun-Young Baek
    Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea.
  • Yun Hwa Roh
    Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
  • Minjung Seong
    Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
  • Yongsik Sim
    From the Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea (Y.S., K.H., B.W.C.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (M.J.C.); Department of Radiology, University Medical Center Freiburg, Freiburg, Germany (E.K.); Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (S. Yune, M.K., S.D.); and Samsung Electronics, Suwon, Republic of Korea (H.K., S. Yang, D.J.L.).
  • Eung Yeop Kim
    Department of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea.
  • Sung Tae Kim
    Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
  • Beomseok Sohn
    Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.