AI-based mid-term effectiveness prediction of bracing treatments for adolescent idiopathic scoliosis.

Abstract

Brace treatment is the standard non-operative treatment of moderate adolescent idiopathic scoliosis (AIS). Yet reliable quantitative prediction of mid-term treatment response remains limited. The mid-term effectiveness of bracing is crucial because the risk of progression begins to increase when the curve exceeds 30° after skeletal maturity has been achieved. This study aimed to develop and externally validate a deep learning regression model to predict the mid-term Cobb angle following brace treatment using baseline and early post-brace radiographs. A retrospective multicenter cohort study was conducted including 294 patients with AIS treated with bracing at two tertiary referral centers. Standing full-spine radiographs were obtained at baseline (pre-brace), immediately post-brace and follow-ups, along with clinical variables including age, sex, and Risser stage. In cross-validation, the model achieved MAEs of 4.06° (T curve; R² = 0.811) and 3.75° (TL/L curve; R² = 0.746). External validation produced MAEs of 5.13° (R² = 0.678) and 3.89° (R² = 0.723) for T and TL/L curves, respectively. This imaging-based deep learning regression model provides precise and interpretative predictions of mid-term bracing outcomes. This approach may assist clinicians in individualized risk stratification and shared decision-making regarding brace management.