Intraoperative Trajectory Realignment Algorithm for Minimizing Screw Misplacement in Cervical Spinal Fusion.

Spinal fusion is a surgical procedure designed to stabilize the spine by connecting vertebrae with screws and rods, where placement accuracy is critical, as even small deviations can cause severe neurovascular complications. The accuracy of traditional preoperative image-based navigation is limited by intraoperative alignment shifts, particularly in the highly mobile cervical spine. This study presents an intraoperative trajectory realignment algorithm that reconciles preoperative computed tomography data with simulated and phantom-derived intraoperative 2-dimensional (2D) projections by integrating forward kinematics, digitally reconstructed radiographs, and optimization-based search strategies. The algorithm was evaluated through computational simulations and physical experiments using a modular 3D-printed cervical spine phantom, with accuracy evaluated through clinical grading and assessment metrics, including entry/end point displacement, angular deviation, and minimum inter-trajectory distance. Results demonstrated high accuracy, with all screws graded as perfectly placed and deviations consistently below clinically relevant thresholds. While these findings confirm the feasibility of this approach under controlled experimental conditions, future validation using cadaveric models and real intraoperative fluoroscopic imaging remains necessary prior to clinical integration.