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Related Experiment Video

Updated: Feb 13, 2026

A Spine Robotic-Assisted Navigation System for Pedicle Screw Placement
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Preoperative evaluation of C2 pedicle screw placement using a deep learning model: Development and validation study.

Junhao Bao1, Wei Wang2, Yuelin Wu1,3

  • 1Spine Department, Orthopaedic Center, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, Guangdong, China.

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Summary

A new deep learning model, C2-Net, accurately assesses C2 pedicle screw placement feasibility. This AI tool offers consistent, rapid evaluations, aiding surgeons in clinical decisions.

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Area of Science:

  • Spinal Surgery
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Preoperative assessment for C2 pedicle screw placement is challenging due to low consistency and high skill requirements.
  • Existing methods for C2 pedicle screw placement assessment are operationally complex and demand significant surgical expertise.

Purpose of the Study:

  • To develop and validate a deep learning model for rapid and accurate assessment of C2 pedicle screw placement feasibility.
  • To create an automated pipeline for C2 pedicle delineation and screw placement probability evaluation.

Main Methods:

  • Developed C2-Net, a deep learning pipeline with image segmentation and screw placement probability modules.
  • Utilized CT images for C2 pedicle delineation and evaluated model performance against 3D-printed screws and surgeons of varying experience.

Main Results:

  • C2-Net achieved 89.4% accuracy, 90.0% sensitivity, and 89.0% specificity on the test set.
  • The model's performance was comparable to senior surgeons and superior to junior surgeons, demonstrating higher consistency.
  • Attention maps provided visual interpretation, and predicted probabilities differentiated C2 pedicle structural variations.

Conclusions:

  • C2-Net demonstrates high accuracy and efficiency in assessing C2 pedicle screw placement, outperforming junior surgeons.
  • The model offers rapid, consistent evaluations and visual interpretations, showing potential as an assistive tool in spinal surgery.
  • C2-Net can aid clinical decision-making for C2 pedicle screw placement.