A New Method for Scoliosis Screening Incorporating Deep Learning With Back Images

Study Design: Retrospective observational study. Objectives: Scoliosis is commonly observed in adolescents, with a world0wide prevalence of 0.5%. It is prone to be overlooked by parents during its early stages, as it often lacks overt characteristics. As a result, many individuals are not aware that they may have scoliosis until the symptoms become quite severe, significantly affecting the physical and mental well-being of patients. Traditional screening methods for scoliosis demand significant physician effort and require unnecessary radiography exposure; thus, implementing large-scale screening is challenging. The application of deep learning algorithms has the potential to reduce unnecessary radiation risks as well as the costs of scoliosis screening. Methods: The data of 247 scoliosis patients observed between 2008 and 2021 were used for training. The dataset included frontal, lateral, and back upright images as well as X-ray images obtained during the same period. We proposed and validated deep learning algorithms for automated scoliosis screening using upright back images. The overall process involved the localization of the back region of interest (ROI), spinal region segmentation, and Cobb angle measurements. Results: The results indicated that the accuracy of the Cobb angle measurement was superior to that of the traditional human visual recognition method, providing a concise and convenient scoliosis screening capability without causing any harm to the human body. Conclusions: The method was automated, accurate, concise, and convenient. It is potentially applicable to a wide range of screening methods for the detection of early scoliosis.