Deep learning

Algorithm

Machine learning

Support vector machine

Radiology

Engineering

Surgery

Civil engineering

Artificial intelligence

Physical therapy

Medical physics

Grading (engineering)

Scoliosis

Idiopathic scoliosis

Radiography

Computer science

Binary classification

Medicine

Physical medicine and rehabilitation

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.

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