Machine learning algorithms for predicting future curve using first and second visit data in female adolescent idiopathic scoliosis patients

Abstract 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 (R 2 ) and median absolute error (MAE). Results For the future curve of PT, MT, and TLL, the top-performing models exhibit R 2 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.