Machine learning-based prediction model for initial correction effect of Cheneau brace in adolescent idiopathic...

Predicting the initial correction rate (ICR) of Cheneau brace treatment for adolescent idiopathic scoliosis (AIS) is critical for personalized therapeutic planning but remains underexplored. This study aims to develop a clinical prediction model for ICR evaluation.

A retrospective analysis included 391 spinal curves from 310 patients with AIS (since some patients have double curves) from 4 orthotic centers. Key variables encompassed demographics, axial trunk rotation (ATR), Risser sign, curve type (C-shaped/S-shaped), apical location (thoracic/lumbar), and prebrace Cobb angle. ICR was calculated as [(prebrace Cobb - postbrace Cobb)/prebrace Cobb] × 100%, with satisfactory correction defined as ICR ≥50%. Logistic regression and random forest models were trained (70% data) and validated (30%). Feature importance was assessed through mean decrease accuracy (MDA) and Gini (MDG). A nomogram integrated significant predictors.

Multivariate analysis identified Risser sign (OR = 0.033, P < 0.001), ATR (OR = 0.853, P < 0.001), curve type (OR = 0.485, P = 0.036), apical location (OR = 5.827, P < 0.001), and BMI (OR = 0.897, P = 0.041) as independent predictors. Random forest confirmed Risser sign as the most influential variable (MDA = 28.07%, MDG = 24.35%), followed by ATR (MDA = 21.99%, MDG = 19.50%). Both models demonstrated high predictive accuracy: logistic regression (AUC = 0.951, accuracy = 0.879) and random forest (AUC = 0.959, accuracy = 0.897). Lumbar curves and C-shaped curves exhibited superior correctability.

Machine learning models effectively predict Cheneau brace ICR, with Risser sign and ATR angle as primary determinants. The developed nomogram provides a practical tool for pretreatment outcome anticipation, enhancing clinical decision-making in AIS management.