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Development and Validation of Deep Learning Preoperative Planning Software for Automatic Lumbosacral Screw Selection

Baodong Wang1, Congying Zou1, Xingyu Liu2,3,4,5

  • 1Department of Orthopedics, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100043, China.

Bioengineering (Basel, Switzerland)
|November 27, 2024
PubMed
Summary

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Comparison of the Predictive Roles of CT- and MRI-Based Pedicle Regional Osteoporosis Status Measurements for Pedicle Screw Loosening After Posterior Lumbar Interbody Fusion.

Global spine journal·2025

Deep learning software automates pedicle screw planning for posterior lumbar interbody fusion (PLIF), significantly improving surgical efficiency and accuracy compared to manual methods.

Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Precise pedicle screw placement is critical in posterior lumbar interbody fusion (PLIF).
  • Manual preoperative planning using CT scans is time-consuming and complex.
  • Limitations in manual planning can impact surgical outcomes.

Purpose of the Study:

  • To develop and validate a deep learning model for automated pedicle screw planning in PLIF.
  • To compare the efficiency and accuracy of automated planning against manual planning.
  • To assess the clinical applicability of AI-driven surgical planning.

Main Methods:

  • A deep learning model was trained on CT data from 228 PLIF patients and validated on 88.
  • The model performed automatic segmentation and screw placement.
Keywords:
Badu gradingGertzbein–Robbins classificationcomputed tomographydeep learningpedicle screwsposterior lumbar interbody fusion

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  • Planning efficiency and accuracy metrics, including Dice coefficient and angular deviations, were evaluated.

    • Main Results:

    • Automatic planning successfully placed screws in all 316 cases, significantly faster than manual planning.
    • High Dice coefficient (0.95) for segmentation accuracy.
    • Angular differences between automatic and manual screw placement were statistically insignificant.
    • 97.7% of automatically planned screws met Gertzbein-Robbins Class A criteria.

    Conclusions:

    • Deep learning software effectively automates pedicle screw planning for PLIF.
    • This AI approach enhances surgical efficiency and accuracy.
    • Automated planning shows potential to improve patient safety and surgical outcomes.