Machine Learning Prediction of Disease Progression in Adolescent Idiopathic Scoliosis

Background and Problem Overview Adolescent Idiopathic Scoliosis (AIS) is a progressive musculoskeletal disorder characterized by a three-dimensional deformation of the spine occurring during adolescence. Diagnosis is typically established with a Cobb angle exceeding 10° and the presence of axial rotation. While the exact etiology remains unknown, leading theories include tissue abnormalities (muscle fibers, bone volume), impaired spinal biomechanics (asymmetric bone growth), and neurological factors (asymmetric cortical thickness, cerebral lateralization, and body schema distortions). The progressive nature of AIS, particularly the high risk of advancement at the onset of puberty, complicates clinical decision-making. Treatment is traditionally divided into three stages: Observation and Exercise: For Cobb angles between 10°-25°. Exercise and Bracing: For Cobb angles between 25°-45°. Surgery: For Cobb angles exceeding 45°. Despite these guidelines, the unpredictable progression of the disease and difficulties in treatment adherence create significant dilemmas. Specifically, for cases on the borderline of surgical indication, clinicians face the challenge of choosing between immediate surgery or conservative monitoring. Currently, there is no definitive method to predict progression, and patients are typically monitored in 6-month intervals. During these intervals, a patient's condition may remain stable or deteriorate significantly. Furthermore, guidelines recommend wearing a brace for an average of 18 hours per day, often for several years. This requirement is physically and psychologically demanding for adolescents, leading to poor compliance due to aesthetic concerns, functional limitations, and skin irritation. The inability to predict progression often leads to overtreatment (unnecessary bracing) or undertreatment (delayed intervention), both of which pose risks to the patient's long-term health. Radiological Concerns Disease progression is monitored via