Artificial intelligence for scoliosis surgical planning and postoperative prediction.
prospective_cohort · Level II
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- Record sourced from PubMed, PMID 42386936.
- Also identified by DOI 10.1038/s41746-026-02934-6.
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Abstract
Adolescent idiopathic scoliosis (AIS) surgery requires precise fusion segment selection and reliable prediction of postoperative alignment, yet current tools lack individualized, validated solutions. We developed ScoliosisPLAN, an AI-based system integrating a YOLOv8-derived segmentation model (ScolioPlanNet) for personalized fusion planning and a latent diffusion model (ScolioPredNet) for simulating postoperative radiographs. In a retrospective development cohort and prospectively collected internal and external validation cohorts of 1425 patients with ≥2-year follow-up, the system achieved performance comparable to experienced surgeons in replicating fusion planning decisions and predicted key radiographic outcomes within clinically acceptable error margins. ScoliosisPLAN provides an interpretable, data-driven framework linking surgical strategy to outcome prediction, supporting standardized, patient-specific decision-making in AIS care.