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Peer-reviewedCC BYnpj Digital Medicine

Artificial intelligence for scoliosis surgical planning and postoperative prediction

  • Zhong He1
  • Neng Lu2
  • Yi Chen1
  • Kenneth Guangpu Yang3
  • Adam Yiu-Chung Lau3
  • Shengru Wang4
  • Shanqi Shen5
  • Elvis Chun-Sing Chui3
  • Zhen Liu1
  • Xiaodong Qin1
  • Zhenhua Feng1
  • Yimu Wang6
10 more authors
  • Yong Qiu1
  • Wayne Yuk-Wai Lee3
  • Jack Chun-Yiu Cheng3
  • Hifza Babar1
  • Jie Chen1
  • Xipu Chen1
  • Minxuan Sun1
  • Dong Xie1
  • Zezhang Zhu1
  • Wu-Jun Li2
6 affiliations
  1. 1Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University
  2. 2National Key Laboratory for Novel Software Technology, School of Computer Science, Nanjing University
  3. 3Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong
  4. 4Department of Orthopedics, Peking Union Medical College Hospital
  5. 5State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University
  6. 6David R. Cheriton School of Computer Science, University of Waterloo
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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.

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Cite

BibTeXRIS
BibTeX
@article{he2026artificial,
  title = {{Artificial intelligence for scoliosis surgical planning and postoperative prediction}},
  author = {Zhong He and Neng Lu and Yi Chen and Kenneth Guangpu Yang and Adam Yiu-Chung Lau and Shengru Wang and Shanqi Shen and Elvis Chun-Sing Chui and Zhen Liu and Xiaodong Qin and Zhenhua Feng and Yimu Wang and Yong Qiu and Wayne Yuk-Wai Lee and Jack Chun-Yiu Cheng and Hifza Babar and Jie Chen and Xipu Chen and Minxuan Sun and Dong Xie and Zezhang Zhu and Wu-Jun Li},
  journal = {npj Digital Medicine},
  year = {2026},
  month = jul,
  doi = {10.1038/s41746-026-02934-6},
  url = {https://doi.org/10.1038/s41746-026-02934-6},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Artificial intelligence for scoliosis surgical planning and postoperative prediction
AU  - Zhong He
AU  - Neng Lu
AU  - Yi Chen
AU  - Kenneth Guangpu Yang
AU  - Adam Yiu-Chung Lau
AU  - Shengru Wang
AU  - Shanqi Shen
AU  - Elvis Chun-Sing Chui
AU  - Zhen Liu
AU  - Xiaodong Qin
AU  - Zhenhua Feng
AU  - Yimu Wang
AU  - Yong Qiu
AU  - Wayne Yuk-Wai Lee
AU  - Jack Chun-Yiu Cheng
AU  - Hifza Babar
AU  - Jie Chen
AU  - Xipu Chen
AU  - Minxuan Sun
AU  - Dong Xie
AU  - Zezhang Zhu
AU  - Wu-Jun Li
PY  - 2026
DA  - 2026/07/01
JO  - npj Digital Medicine
DO  - 10.1038/s41746-026-02934-6
UR  - https://doi.org/10.1038/s41746-026-02934-6
AB  - 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.
SN  - 2398-6352
ER  -