Objective: The purpose of this study was to evaluate the ability of a novel artificial intelligence (AI) model in identifying optimized transpedicular screw trajectories with higher bone mineral density (BMD) as well as higher pull-out force (POF) in osteoporotic patients.
Methods: An innovative pedicle screw trajectory planning system called Bone's Trajectory was developed using a 3D graphic search and an AI-based finite element analysis model. The preoperative CT scans of 21 elderly osteoporotic patients were analyzed retrospectively. The AI model automatically calculated the number of alternative transpedicular trajectories, the trajectory BMD, and the estimated POF of L3-5. The highest BMD and highest POF of optimized trajectories were recorded and compared with AO standard trajectories.
Results: The average patient age and average BMD of the vertebral bodies were 69.6 ± 7.8 years and 55.9 ± 17.1 mg/ml, respectively. On both sides of L3-5, the optimized trajectories showed significantly higher BMD and POF than the AO standard trajectories (p < 0.05). On average, the POF of optimized trajectory screws showed at least a 2.0-fold increase compared with AO trajectory screws.
Conclusions: The novel AI model performs well in enabling the selection of optimized transpedicular trajectories with higher BMD and POF than the AO standard trajectories.
Keywords: artificial intelligence; osteoporosis; screw trajectory; surgical planning.