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Automated multi-trajectory planning for C1-C2 screw fixation using CT-derived 3D models.

Yau-Zen Chang1,2, Sanny Kumar Sahani1, Shih-Lin Wu1

  • 1Chang Gung University, 333323, Taoyuan, Taiwan.

European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
|June 24, 2026
PubMed
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An automated 3D planning pipeline for C1-C2 screw fixation successfully generated multiple safe screw trajectories, demonstrating its potential as a decision-support tool for spinal surgery.

Area of Science:

  • Spinal surgery and medical imaging analysis.
  • Development of automated surgical planning tools.
  • Application of artificial intelligence in orthopedic surgery.

Background:

  • Cervical spine (C1-C2) fixation is critical for spinal stability.
  • Accurate screw placement is essential to avoid neurological injury.
  • Current planning methods can be time-consuming and may lack sufficient trajectory options.

Purpose of the Study:

  • To evaluate an automated computed tomography (CT)-derived 3D model-based planning pipeline for C1-C2 screw fixation.
  • To test if a redundancy-by-design approach yields at least one safe (Grade 0-1) screw trajectory candidate per side.
  • To assess the feasibility of a reproducible preoperative decision-support framework.

Main Methods:

  • Retrospective study using CT-derived 3D surface models and PointNet++ for segmentation.
Keywords:
C1–C2 fixationDeep learningGeometric analysisPreoperative planningScrew trajectory planning

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  • Generation of multiple screw trajectory candidates for C1 and C2 vertebrae.
  • Independent grading of candidate trajectories by spine surgeons using the Gertzbein-Robbins scale.
  • Validation on internal and external datasets (VerSe 2019).
  • Main Results:

    • High segmentation performance (mean mIoU of 88.8%) and accurate landmark localization (<1 mm error).
    • High rate of safe (Grade 0-1) screw trajectory candidates: 93.6% by conservative consensus on the internal set and 88.5% on the external set.
    • Every evaluable side achieved at least one Grade 0-1 candidate trajectory; substantial inter-rater agreement was observed.

    Conclusions:

    • The automated CT-derived 3D planning pipeline effectively generates multiple safe C1-C2 screw trajectory candidates.
    • The redundancy-by-design strategy shows preclinical feasibility for a reproducible preoperative decision-support framework.
    • Further multicenter and prospective validation is needed prior to clinical implementation.