Evaluating CNN Architectures for the Automated Detection and Grading of Modic Changes in MRI: A Comparative Study.

Journal: Orthopaedic surgery
Published Date: Dec 5, 2024

Abstract

OBJECTIVE: Modic changes (MCs) classification system is the most widely used method in magnetic resonance imaging (MRI) for characterizing subchondral vertebral marrow changes. However, it shows a high degree of sensitivity to variations in MRI because of its semiquantitative nature. In 2021, the authors of this classification system further proposed a quantitative and reliable MC grading method. However, automated tools to grade MCs are lacking. This study developed and investigated the performance of convolutional neural network (CNN) in detecting and grading MCs based on their maximum vertical extent. In order to verify performance, we tested CNNs' generalization performance, the performance of CNN with that of junior doctors, and the consistency of junior doctors after AI assistance.

Authors

  • Li-Peng Xing
    State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences & Biomedical Engineering, Hebei University of Technology, Tianjin, China.
  • Gang Liu
    Department of Interventional Radiology, Qinghai Red Cross Hospital, Xining, Qinghai, China.
  • Hao-Chen Zhang
    State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences & Biomedical Engineering, Hebei University of Technology, Tianjin, China.
  • Lei Wang
    Department of Nursing, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Shan Zhu
    Tianjin Hospital, Tianjin 300211, P.R. China.
  • Man Du La Hua Bao
    Department of Spine Surgery, Tianjin Hospital, Tianjin University, Tianjin, China.
  • Yan-Ni Wang
    Department of Spine Surgery, Tianjin Hospital, Tianjin University, Tianjin, China.
  • Chao Chen
    Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Zhi Wang
    Department of Pharmacy, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Xin-Yu Liu
    Department of Gastroenterology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing 100730, China.
  • Shuai Zhang
    School of Information, Zhejiang University of Finance and Economics, Hangzhou, China.
  • Qiang Yang

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