Machine Learning and Deep Learning for Diagnosis of Lumbar Spinal Stenosis: Systematic Review and Meta-Analysis.

Journal: Journal of medical Internet research
PMID: 39715552

Abstract

BACKGROUND: Lumbar spinal stenosis (LSS) is a major cause of pain and disability in older individuals worldwide. Although increasing studies of traditional machine learning (TML) and deep learning (DL) were conducted in the field of diagnosing LSS and gained prominent results, the performance of these models has not been analyzed systematically.

Authors

  • Tianyi Wang
    College of Physical Education, Qiqihar University, Qiqihar 161000, China.
  • Ruiyuan Chen
    Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
  • Ning Fan
    Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, 5 JingYuan Road, Shijingshan District, Beijing, 100043, China.
  • Lei Zang
    Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, 5 JingYuan Road, Shijingshan District, Beijing, 100043, China. zanglei@ccmu.edu.cn.
  • Shuo Yuan
  • Peng Du
    Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
  • Qichao Wu
    Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
  • Aobo Wang
    Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, 5 JingYuan Road, Shijingshan District, Beijing, 100043, China.
  • Jian Li
    Fujian Key Laboratory of Traditional Chinese Veterinary Medicine and Animal Health, College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, China.
  • Xiaochuan Kong
    Department of Orthopaedics, West Campus, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China. Electronic address: kxch_xiaochuan@163.com.
  • Wenyi Zhu
    Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.

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