AI-driven Assessment of Vertebral Rotation in Adolescent Idiopathic Scoliosis Using Weight-bearing Cone-beam CT

RATIONALE AND OBJECTIVES: To evaluate an artificial intelligence (AI)-based algorithm for quantifying vertebral rotation angles on weight-bearing cone-beam CT images in adolescent idiopathic scoliosis (AIS) patients, and to investigate how well the 2D Nash & Moe grading system represents 3D vertebral rotation under weight-bearing conditions. MATERIALS AND METHODS: This prospective study included 72 adolescents with AIS. Six predefined vertebrae per patient were manually measured by two radiologists, while all vertebrae were also processed using a U-Net-based algorithm to obtain automated rotation angles. Nash & Moe grades were assessed from standing radiographs. Agreement was evaluated using intra-class correlation coefficients (ICCs), Bland-Altman analysis, and correlation tests. The explanatory power of Nash & Moe grades for 3D vertebral rotation was analyzed by linear regression. RESULTS: Automated vertebral rotation angles demonstrated good agreement and strong correlation with manual measurements (ICC = 0.881, 95% CI 0.852-0.905; Spearman correlation coefficient = 0.900, 95% CI 0.871-0.920). Linear regression showed strong relationships between automated and manual measurements for thoracic (R² = 0.776) and lumbar vertebrae (R² = 0.792). Nash & Moe grades exhibited moderate explanatory power for 3D rotation measured by the algorithm (R² = 0.549 thoracic and R² = 0.628 lumbar), with similar results for manual measurements (R² = 0.532 thoracic; R² = 0.621 lumbar). CONCLUSION: The AI-driven algorithm showed good agreement with manual measurements and enabled reliable automated quantification of vertebral rotation under weight-bearing conditions, whereas the 2D Nash & Moe method provides only approximate estimates and lacks precision for detailed assessment. This automated 3D approach may improve scoliosis evaluation and support more informed clinical decision-making in AIS management.