Automated multi-objective pedicle screw planning

Posterior spinal fusion is a common procedure, but manual pedicle screw planning is time-consuming, cognitively demanding, and may not fully leverage patient-specific anatomy. Pedicle screws are vulnerable to loosening, a complication linked to poor anchorage. This study aimed to develop and evaluate an automated pedicle screw planning framework for multi-level fusion, designed to simultaneously optimize clinical safety, biomechanical stability, and screw head alignment.

The proposed automated screw planning workflow was compared with clinical standard manual planning on 20 clinical computed tomography (CT) scans comprising 238 pedicle screws. The automated pipeline utilized neural network-based segmentation, template mesh fitting, and multi-objective optimization balancing clinical safety, biomechanical stability, and rod conformity. Clinical safety was assessed using the Gertzbein-Robbins classification system. Comparative analysis evaluated bone density coverage via Hounsfield unit (HU) measurements and screw head alignment through angular misalignment calculations.

Clinical safety assessment demonstrated high anatomical accuracy, with 87.8% grade A and 12.2% grade B screws per the Gertzbein-Robbins scale, with a median grade B breach of 0.8 mm. The automated method also achieved a median relative improvement of 74.9% (P<0.001) in HU coverage and a median relative improvement of 58.6% (P<0.001) in screw-head misalignment. The proposed automated pedicle screw planning pipeline yields clinically safe screw plans that may improve screw stability while reducing rod bending requirements.

The assessed approach carries significant clinical potential in simplifying the intraoperative workflow while improving the surgical outcome.