Development and validation of deep learning algorithms for scoliosis screening using back images.

Journal: Communications biology
Published Date: Oct 25, 2019

Abstract

Adolescent idiopathic scoliosis is the most common spinal disorder in adolescents with a prevalence of 0.5-5.2% worldwide. The traditional methods for scoliosis screening are easily accessible but require unnecessary referrals and radiography exposure due to their low positive predictive values. The application of deep learning algorithms has the potential to reduce unnecessary referrals and costs in scoliosis screening. Here, we developed and validated deep learning algorithms for automated scoliosis screening using unclothed back images. The accuracies of the algorithms were superior to those of human specialists in detecting scoliosis, detecting cases with a curve ≥20°, and severity grading for both binary classifications and the four-class classification. Our approach can be potentially applied in routine scoliosis screening and periodic follow-ups of pretreatment cases without radiation exposure.

Authors

  • Junlin Yang
    1Spine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Kai Zhang
    Anhui Province Key Laboratory of Respiratory Tumor and Infectious Disease, First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
  • Hengwei Fan
    1Spine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Zifang Huang
    4Department of Spine Surgery, the 1st Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong China.
  • Yifan Xiang
    2State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong China.
  • Jingfan Yang
    1Spine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Lin He
    College of Plant Protection, Southwest University, Chongqing, China.
  • Lei Zhang
    Division of Gastroenterology, Union Hospital, Tongji Medical College Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yahan Yang
    University of Pennsylvania, Philadelphia, PA.
  • Ruiyang Li
    2State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong China.
  • Yi Zhu
    2State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong China.
  • Chuan Chen
    Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Fan Liu
    Hunan Provincial Key Laboratory of Dong Medicine, Hunan University of Medicine, Huaihua, China.
  • Haoqing Yang
    3School of Computer Science and Technology, Xidian University, Xi'an, Shanxi China.
  • Yaolong Deng
    1Spine Center, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Weiqing Tan
    6Health Promotion Centre for Primary and Secondary Schools of Guangzhou Municipality, Guangzhou, Guangdong China.
  • Nali Deng
    6Health Promotion Centre for Primary and Secondary Schools of Guangzhou Municipality, Guangzhou, Guangdong China.
  • Xuexiang Yu
    7Department of Sports and Arts, Guangzhou Sport University, Guangzhou, Guangdong China.
  • Xiaoling Xuan
    Xinmiao Scoliosis Prevention of Guangdong Province, Guangzhou, Guangdong China.
  • Xiaofeng Xie
  • Xiyang Liu
    School of Computer Science and Technology, Xidian University, No. 2 South Taibai Rd, Xi'an, 710071, China. xyliu@xidian.edu.cn.
  • Haotian Lin
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou.

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