Bone Age Assessment Using Artificial Intelligence in Korean Pediatric Population: A Comparison of Deep-Learning Models Trained With Healthy Chronological and Greulich-Pyle Ages as Labels.

Journal: Korean journal of radiology
Published Date:

Abstract

OBJECTIVE: To develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by comparing it with a Greulich-Pyle-based deep-learning model.

Authors

  • Pyeong Hwa Kim
    Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Hee Mang Yoon
    1 Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea.
  • Jeong Rye Kim
    1 Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea.
  • Jae-Yeon Hwang
    Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea.
  • Jin-Ho Choi
    Department of Pediatrics, Asan Medical Center Children's Hospital, University of Ulsan College of Medicine, Seoul, Korea.
  • Jisun Hwang
    Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea.
  • Jaewon Lee
    Bio-Intelligence & Data Mining Laboratory, School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea.
  • Jinkyeong Sung
    From the R&D Center, VUNO, 507 Gangnamdae-ro, Seocho-gu, Seoul 06536, South Korea (J.S., W.B., B.P., E.J., K.H.J.); and Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (S.P., S.M.L., J.B.S.).
  • Kyu-Hwan Jung
    VUNO Inc., Seoul, Korea.
  • Byeonguk Bae
    Kakao, Seoul, South Korea.
  • Ah Young Jung
    Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Young Ah Cho
    1 Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea.
  • Woo Hyun Shim
    Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Boram Bak
    University of Ulsan Foundation for Industry Cooperation, Ulsan, Korea.
  • Jin Seong Lee
    1 Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea.