Automated Cobb angle measurement in scoliosis radiographs: A deep learning approach for screening

Abstract

Adolescent idiopathic scoliosis is the most common paediatric spinal deformity, impacting 1 in 300 children.1 In Singapore and other countries, national screening programmes have been established to detect scoliosis early, with the aim of using bracing to prevent progression to moderate or severe scoliosis, which may require surgical intervention.1,2 Whole spine radiography is crucial for accurately diagnosing scoliosis using the Cobb method, where scoliosis is defined by a Cobb angle of at least 10°.3 This method requires precise identification of the most tilted vertebral endplates above and below the curve apex, leading to a classification of mild (10–25°), moderate (25–40°) or severe scoliosis (>40°).4