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

Authors

Jianrong He, Zejun Liang, Fanzhuang Rong, Zengtong Chen, Hanyu Li, Jin Liu, Yu Zhang, Yiteng Zhang, Meng Zhang, Han Zheng, Zhenlin Li, Jing Tang

Published in

Academic radiology. Aug 12, 2026. Epub Aug 12, 2026.

Abstract

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.

PMID:
42586897
Bibliographic data and abstract were imported from PubMed on 13 Aug 2026.

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