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<p>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.</p>

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Artificial intelligence for scoliosis surgical planning and postoperative prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

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  • Zhong He,
  • Neng Lu,
  • Yi Chen,
  • Kenneth Guangpu Yang,
  • Adam Yiu-Chung Lau,
  • Shengru Wang,
  • Shanqi Shen,
  • Elvis Chun-Sing Chui,
  • Zhen Liu,
  • Xiaodong Qin,
  • Zhenhua Feng,
  • Yimu Wang,
  • Yong Qiu,
  • Wayne Yuk-Wai Lee,
  • Jack Chun-Yiu Cheng,
  • Hifza Babar,
  • Jie Chen,
  • Xipu Chen,
  • Minxuan Sun,
  • Dong Xie,
  • Zezhang Zhu,
  • Wu-Jun Li

摘要

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.