Automatic diagnosis and grading of patellofemoral osteoarthritis from the axial radiographic view: a deep learning-based approach.

Journal: Acta radiologica (Stockholm, Sweden : 1987)
Published Date: Apr 12, 2022

Abstract

BACKGROUND: Patellofemoral osteoarthritis (PFOA) has a high prevalence and is assessed on axial radiography of the patellofemoral joint (PFJ). A deep learning (DL)-based approach could help radiologists automatically diagnose and grade PFOA via interpreting axial radiographs.

Authors

  • Tuya E
    Department of Radiology, 26447Peking University First Hospital, Beijing, PR China.
  • Cen Wang
    Department of Radiology, Beijing Nuclear Industry Hospital, Beijing, PR China.
  • Yingpu Cui
    Department of Radiology, Peking University First Hospital, 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
  • Rile Nai
    Department of Radiology, 26447Peking University First Hospital, Beijing, PR China.
  • Yaofeng Zhang
    Beijing Smart Tree Medical Technology co. Ltd., Beijing, China.
  • Xiaodong Zhang
    The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • XiaoYing Wang

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