In Communications biology
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.
Yang Junlin, Zhang Kai, Fan Hengwei, Huang Zifang, Xiang Yifan, Yang Jingfan, He Lin, Zhang Lei, Yang Yahan, Li Ruiyang, Zhu Yi, Chen Chuan, Liu Fan, Yang Haoqing, Deng Yaolong, Tan Weiqing, Deng Nali, Yu Xuexiang, Xuan Xiaoling, Xie Xiaofeng, Liu Xiyang, Lin Haotian
Bone, Translational research