Deep learning model for the automated detection and classification of central canal and neural foraminal stenosis upon cervical spine magnetic resonance imaging.

Journal: BMC medical imaging
PMID: 39593012

Abstract

BACKGROUND: A deep learning (DL) model that can automatically detect and classify cervical canal and neural foraminal stenosis using cervical spine magnetic resonance imaging (MRI) can improve diagnostic accuracy and efficiency.

Authors

  • Enlong Zhang
    Department of Radiology, Peking University International Hospital, Beijing, People's Republic of China.
  • Meiyi Yao
    Institute of Computing Technology, Chinese Academy of Sciences, Beijing, PR China (M.Y., X.S., S.J.).
  • Yuan Li
    NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
  • Qizheng Wang
    Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
  • Xinhang Song
    Institute of Computing Technology, Chinese Academy of Sciences, Beijing, PR China (M.Y., X.S., S.J.).
  • Yongye Chen
    Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
  • Ke Liu
    State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, P.R. China.
  • Weili Zhao
    Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. zhao.weili@yahoo.com.
  • Xiaoying Xing
    Department of Radiology, Peking University Third Hospital, Haidian District, 49 North Garden Road, Beijing, 100191, People's Republic of China.
  • Yan Zhou
    Department of Computer Science, University of Texas at Dallas, Richardson, Texas 75080, United States.
  • Fanyu Meng
    Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
  • Hanqiang Ouyang
    Department of Orthopedics, Peking University Third Hospital, Beijing, 100191, China.
  • Gongwei Chen
    Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
  • Liang Jiang
    College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, 518055, China. Electronic address: fredjiang240@126.com.
  • Ning Lang
    Department of Radiology, Peking University Third Hospital, Beijing 10019, China.
  • Shuqiang Jiang
  • Huishu Yuan
    Department of Radiology, Peking University Third Hospital, Beijing 10019, China.

Keywords

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