A Fully Automated Multistage Deep Learning System for Lenke Classification: Enhanced Diagnostic Precision in Adolescent Idiopathic Scoliosis.

Journal: The Journal of bone and joint surgery. American volume
Published Date: Dec 29, 2025

Abstract

BACKGROUND: The Lenke classification for adolescent idiopathic scoliosis (AIS) has interobserver variability due to subjective clinical assessment. We developed and validated a fully automated deep learning system for precise Lenke classification using spinal radiographs. METHODS: This retrospective study included 650 individuals (mean age, 13.75 ± 2.23 years; 433 female, 217 male; 618 Han Chinese, 32 Tibetan), comprising 183 healthy controls and 467 patients with AIS (aged 10 to 18 years; 25° ≤ Cobb angle < 90°) with full-spine radiographs. A multistage deep learning system consisting of (1) Swin-Unet segmentation of vertebrae (C7-S1) for automated Cobb angle measurement, (2) DeepLabv3+ localization of lumbar pedicles (L1-L5) to determine modifiers via the centroid-to-CSVL (central sacral vertebral line) distance, and (3) a fusion module integrating features to curve types and lumbar (A/B/C) and sagittal thoracic (-/N/+) modifiers was designed to perform end-to-end Lenke classification automatically. Validation used an independent test set. RESULTS: The system achieved 95.6% overall accuracy in Lenke classification and had a macro-averaged F1 score of 0.862. Vertebral segmentation attained Dice coefficients of 0.917 (anteroposterior) and 0.942 (lateral). Cobb angle measurements showed excellent agreement with those of experts (intraclass correlation coefficient, 0.969 to 0.976 for thoracic or thoracolumbar/lumbar curves). Modifier assignment achieved F1 scores of 0.912 (lumbar A/B/C) and 0.928 (sagittal -/N/+), exceeding clinical acceptability thresholds. CONCLUSIONS: The fully automated system was able to perform rapid, objective, interpretable, and clinically reliable classification of the Lenke type directly from radiographs, with performance comparable with that of expert assessment. It demonstrates potential for standardizing AIS surgical planning, reducing diagnostic variability, and improving surgical workflow efficiency. LEVEL OF EVIDENCE: Diagnostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Authors

  • Lingcong Xu
    Department of Orthopedics, Sichuan Provincial People's Hospital School of Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
  • Caiyuan Liu
    Department of Orthopedics, Sichuan Provincial People's Hospital School of Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
  • Wenjie Zhong
    Nepean Urology Research Group, Nepean Hospital, Kingswood, Australia.
  • Ke Xu
    Mechatronics Engineering of University of Electronic Science and Technology of China, Chengdu, 611731, China.
  • Niezhenghao He
    School of Medicine, University of Electronic Science and Technology of China, Chengdu Sichuan, 610054, P. R. Chnia.
  • Jiang Hu
    Department of Orthopedics, Sichuan Academy of Medical Science·Sichuan Provincal People's Hospital, Chengdu Sichuan, 610072, [email protected].
  • Kun Zhang
    Philosophy Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
  • Haowen Cui
    Department of Orthopedics, Sichuan Provincial People's Hospital School of Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.

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