Purpose: This study was designed to develop a machine learning (ML) model that predicts future Cobb angle in patients with adolescent idiopathic scoliosis (AIS) using minimal radiographs and simple questionnaires during the first and second visits.
Methods: Our study focused on 887 female patients with AIS who were initially consulted at a specialized scoliosis center from July 2011 to February 2023. Patient data, including demographic and radiographic data based on anterior-posterior and lateral whole-spine radiographs, were collected at the first, second, and final visits. ML algorithms were employed to develop individual regression models for future Cobb angles of each curve type (proximal thoracic: PT, main thoracic: MT, and thoracolumbar/lumbar: TLL) using PyCaret in Python. Multiple models were explored and analyzed, with the selection of optimal models based on the coefficient of determination (R2) and median absolute error (MAE).
Results: For the future curve of PT, MT, and TLL, the top-performing models exhibit R2 of 0.73, 0.63, and 0.61 and achieve MAE of 2.3°, 4.0°, and 4.2°.
Conclusions: The ML-based model using items commonly evaluated at the first and second visits accurately predicted future Cobb angles in female patients with AIS.
Keywords: Adolescent idiopathic scoliosis; Classification model; Machine learning algorithms; Prediction model; Regression model.
© 2025. The Author(s).