Abstract
The assessment of coronal balance is critical for cosmetic appearance and quality of life in patients with adolescent idiopathic scoliosis (AIS), yet its manual quantification remains constrained by substantial workflow demands and inherent subjectivity. While artificial intelligence has shown promise in automated Cobb angle measurement, robust and clinically safety-aware solutions for coronal balance parameters are still lacking. In this study, we developed and validated a robust HRNet-based automated system for quantifying Shoulder Height Difference (SHD), Pelvic Obliquity (PO), and C7 Plumb Line Offset (C7 offset) using a dataset of 847 full-spine radiographs. Beyond anatomically driven topological constraints, we integrated a novel Clinical Safety Gating Mechanism that proactively suppresses unreliable outputs in cases of anatomical ambiguity, functioning as a safety gating by design. The system achieved millimeter-level precision (mean absolute error: 0.86–2.45 mm) and demonstrated strong agreement with manual reference measurements (Pearson r > 0.95), with overall performance approximating the inter-rater variability among senior spinal surgeons. Importantly, the deterministic algorithmic consistency of the system mitigates human fatigue and subjective fluctuation, offering theoretical advantages for future longitudinal assessments. Collectively, this work positions the automated coronal balance system not merely as a measurement tool, but as a safety-aware quality safety gating mechanism. These findings validate the technical feasibility and internal safety of the model, providing a robust foundation for large-scale clinical data auditing and future prospective studies on long-term patient outcomes.
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Data availability
The radiographic datasets generated and/or analysed during the current study are not publicly available due to patient privacy considerations and institutional regulations but are available from the corresponding author on reasonable request.
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Acknowledgements
The authors gratefully acknowledge the support of the First Affiliated Hospital of Xinjiang Medical University for assistance with data acquisition and quality control, and the Xinjiang Medical-Engineering Integration Technology Center for their help in experimental environment setup and technical implementation.
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This work was supported by the “Tianchi Talent” Introduction Program. The funding body had no role in the study design, data collection, analysis, interpretation, or manuscript preparation.
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Z.W. and Y.Z. conceived the study, designed the methodology, and developed the AI model. Z.W. performed the primary data analysis and drafted the manuscript. F.X. participated in data validation, clinical interpretation, and quality control. A.M., Y.L., and J.Z. participated in data collection, image annotation and performed the statistical analysis. W.Z. supervised the study, managed the project, acquired funding, and revised the manuscript. Y.Y. provided senior academic supervision and critical revision of the manuscript. All authors read and approved the final manuscript.
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This retrospective study was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (approval ID: K202509-69). All methods were carried out in accordance with relevant guidelines and regulations (e.g., the Declaration of Helsinki). Owing to the retrospective design of the study and the use of anonymized data, the requirement for informed consent was waived by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University.
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Wang, Z., Zhang, Y., Xue, F. et al. Automated quantification of coronal balance in spinal deformity: a safety-aware clinical workflow. Sci Rep (2026). https://doi.org/10.1038/s41598-026-47402-z
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DOI: https://doi.org/10.1038/s41598-026-47402-z
