Deep learning in Cobb angle automated measurement on X-rays: a systematic review and meta-analysis.

Journal: Spine deformity
Published Date:

Abstract

PURPOSE: This study aims to provide an overview of different deep learning algorithms (DLAs), identify the limitations, and summarize potential solutions to improve the performance of DLAs.

Authors

  • Yuanpeng Zhu
    Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.
  • XiangJie Yin
    Department of Spine Surgery, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China.
  • Zefu Chen
    Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; State Key Laboratory of Complex Severe and Rare Diseases, Beijing 100730, China; Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China; Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China; DISCO (Deciphering disorders Involving Scoliosis and COmobidities) study group.
  • Haoran Zhang
    Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Kexin Xu
    Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.
  • Jianguo Zhang
    College of Automation, Harbin Engineering University, No. 145, Nantong street, Harbin, China.
  • Nan Wu
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, Departments of Computational Biology and Structural Biology, School of Medicine , University of Pittsburgh , Pittsburgh , Pennsylvania 15261 , United States.