Research on a deep learning-based model for measurement of X-ray imaging parameters of atlantoaxial joint.

Journal: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
Published Date: Jul 10, 2025

Abstract

PURPOSE: To construct a deep learning-based SCNet model, in order to automatically measure X-ray imaging parameters related to atlantoaxial subluxation (AAS) in cervical open-mouth view radiographs, and the accuracy and reliability of the model were evaluated.

Authors

  • Yuhua Wu
    The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China.
  • Yuwen Zheng
    The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China.
  • Jinping Zhu
    Xi'an Daxing Hospital, Xi'an, China.
  • Xiaofei Chen
    Oncology Biometrics, AstraZeneca, Gaithersburg, Maryland, USA.
  • Fuwen Dong
    Department of Radiology, Gansu Provincial Hospital of Traditional Chinese Medicine (The first affiliated hospital of Gansu University of Traditional Chinese Medicine), Lanzhou, 730050, Gansu, China.
  • Linyang He
    Hangzhou Jianpei Technology Co., Ltd, Hangzhou, China.
  • Jinyang Zhu
    Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2 Liutiao Road, Changchun 130023, PR China.
  • Guohua Cheng
    Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China.
  • Ping Wang
    School of Chemistry and Chemical Engineering, Shandong University of Technology, 255049, Zibo, PR China. Electronic address: wangping876@163.com.
  • Sheng Zhou
    Department of The First Clinical Medical College of Gansu, University of Chinese Medicine, Lanzhou, Gansu, China.

Keywords

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