Sagittal intervertebral rotational motion: a deep learning-based measurement on flexion-neutral-extension cervical lateral radiographs.

Journal: BMC musculoskeletal disorders
Published Date: Nov 8, 2022

Abstract

BACKGROUND: The analysis of sagittal intervertebral rotational motion (SIRM) can provide important information for the evaluation of cervical diseases. Deep learning has been widely used in spinal parameter measurements, however, there are few investigations on spinal motion analysis. The purpose of this study is to develop a deep learning-based model for fully automated measurement of SIRM based on flexion-neutral-extension cervical lateral radiographs and to evaluate its applicability for the flexion-extension (F/E), flexion-neutral (F/N), and neutral-extension (N/E) motion analysis.

Authors

  • Yuting Yan
    School of Agricultural Engineering, Jiangsu University Zhenjiang 212013 People's Republic of China yanyuting@ujs.edu.cn maohp@ujs.edu.cn +86 511 88797338 +86 511 88797338.
  • Xinsheng Zhang
    The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou 310053 Zhejiang, China.
  • Yu Meng
    Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, Zhejiang, China.
  • Qiang Shen
    Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Linyang He
    Hangzhou Jianpei Technology Co., Ltd, Hangzhou, 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.
  • Xiangyang Gong
    Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, Zhejiang, China.; Institute of Artificial Intelligence and Remote Imaging, Hangzhou Medical College, Hangzhou 310014, China. Electronic address: gong.xy@vip.163.com.

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