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January 24, 2026Current Directions in Biomedical Engineering0 citationsOpen Access

Preoperative Pedicle Screw Placement Planning using Particle Swarm Optimization

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FKF KrummenauerFriedrich-Alexander-Universität Erlangen-NürnbergIFIvan Alexander Trujillo FreireFriedrich-Alexander-Universität Erlangen-NürnbergWMWaseem Masalha

Key Points

  • The central aim is to optimize pedicle screw placements in scoliosis surgery using advanced algorithms.
  • Computed screw placements from CT scans of vertebrae.
  • Mapped entry points using a specialized dictionary.
  • Optimized placements with particle swarm optimization.
  • Evaluated placements using Gertzbein-Robbins and Zdichavsky grading systems.
  • Assessed by an experienced surgeon.
  • 89.58% of all placements were valid.
  • 46.51% of valid placements were rated an A according to Gertzbein-Robbins.
  • 53.49% were rated as B in the same classification.
  • All valid placements received an Ia rating according to the Zdichavsky system.
  • 41.86% were optimal, and 20.93% were considered good placements.

Abstract

Abstract Correct pedicle screw placement is essential in scoliosis surgery to ensure patient safety and surgical success. We propose a method to compute screw placements from vertebrae CT scans by mapping the entry point from a dictionary and then optimize the placement with particle swarm optimization. Our approach was tested on T6, T12, and L4 vertebrae and evaluated using the Gertzbein-Robbins classification (GR), the Zdichavsky grading system (ZG). Furthermore, a specialized surgeon assessed the placements. The results indicate that 89.58% of the total placements were valid. Among these valid placements, 46.51% were rated an A according to the GR and the other 53.49% were rated as B. All valid placements were Ia according to ZG. The medical assessment showed that 41.86% and 20.93% were optimal placements and good placements respectively.

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Cite This Study

Krummenauer et al. (2025) studied this question.

synapsesocial.com/papers/6974610cbb9d90c67120ae89https://doi.org/10.1515/cdbme-2025-0324
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