Two-Stage Deep Learning Model for Diagnosis of Lumbar Spondylolisthesis Based on Lateral X-Ray Images.

Journal: World neurosurgery
PMID: 38608811

Abstract

BACKGROUND: Diagnosing early lumbar spondylolisthesis is challenging for many doctors because of the lack of obvious symptoms. Using deep learning (DL) models to improve the accuracy of X-ray diagnoses can effectively reduce missed and misdiagnoses in clinical practice. This study aimed to use a two-stage deep learning model, the Res-SE-Net model with the YOLOv8 algorithm, to facilitate efficient and reliable diagnosis of early lumbar spondylolisthesis based on lateral X-ray image identification.

Authors

  • Chunyang Xu
    Department of orthopedics, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
  • Xingyu Liu
    First People's Hospital of Zunyi City, Zunyi, China.
  • Beixi Bao
    Department of orthopedics, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
  • Chang Liu
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Runchao Li
    Longwood Valley Medical Technology Co Ltd, Beijing, China.
  • Tianci Yang
    Department of orthopedics, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
  • Yukan Wu
    Department of orthopedics, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
  • Yiling Zhang
    Department of Otolaryngology Head and Neck Surgery，the Second Xiangya Hospital，Central South University，Changsha，410011，China.
  • Jiaguang Tang
    Department of orthopedics, Beijing Tongren Hospital, Capital Medical University, Beijing, China. Electronic address: tangjiaguang2023@163.com.

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