Results of using robotic-assisted navigational system in pedicle screw placement

Recent technical developments have resulted in robotic-assisted pedicle screw placement techniques. However, the use of robotic-assisted navigational techniques is still subject to controversy. This study aims to assess the accuracy and safety of a self-developed navigation system, the point spine navigation system (PSNS), for robotic-assisted pedicle screw placement surgery. Fifty-nine pedicle screws were implanted in three porcine vertebrae at the T6–T10 and L1–L5 levels, with the assistance of the PSNS. The navigation and planning system provides virtual surgical guide images, including sagittal, coronal, axial, oblique planes, and customized three-dimensional reconstructions for each vertebra to establish accurate pedicle screw trajectories and placement tracts. After pedicle screw placement, post-operative spiral computer tomographic scans were performed and screws were evaluated using the Gertzbein–Robbins classification. Differences between the actual pedicle screw position and pre-operative planning paths, including the angle, shortest distance, and entry trajectory were recorded. The 59 pedicle screw placements were all within a safe zone, and there was no spinal canal perforation or any other damage under postoperative computed tomography image data. Fifty-one screws were categorized as group A, seven screws were noted as group B, and one screw was identified as group E under the Gertzbein–Robbins classification. The mean entry point deviation was 2.71 ± 1.72, mean trajectory distance was 1.56 ± 0.66 mm, and average shortest distance between two paths was 0.96 ± 0.73 mm. Pedicle placement remains a challenging procedure with high reported incidences of nerve and vascular injuries. The implementation of a robotic-assisted navigational system yields an acceptable level of accuracy and safety for the pedicle screw placement surgery. © 2019 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.