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Introduction: Pedicle screws are commonly employed to restore spinal stability and correct deformities. The Renaissance robotic system was developed to improve the accuracy of pedicle screw placement., Purpose: In this study, we developed an intraoperative classification system for evaluating the accuracy of pedicle screw placements through secondary registration. Furthermore, we evaluated the benefits of using the Renaissance robotic system in pedicle screw placement and postoperative evaluations. Finally, we examined the factors affecting the accuracy of pedicle screw implantation., Results: Through use of the Renaissance robotic system, the accuracy of Kirschner-wire (K-wire) placements deviating <3 mm from the planned trajectory was determined to be 98.74%. According to our classification system, the robot-guided pedicle screw implantation attained an accuracy of 94.00% before repositioning and 98.74% after repositioning. However, the malposition rate before repositioning was 5.99%; among these placements, 4.73% were immediately repositioned using the robot system and 1.26% were manually repositioned after a failed robot repositioning attempt. Most K-wire entry points deviated caudally and laterally., Conclusion: The Renaissance robotic system offers high accuracy in pedicle screw placement. Secondary registration improves the accuracy through increasing the precision of the positioning; moreover, intraoperative evaluation enables immediate repositioning. Furthermore, the K-wire tends to deviate caudally and laterally from the entry point because of skiving, which is characteristic of robot-assisted pedicle screw placement.

Background: The aim of this study was to investigate the learning curve of robotic spine surgery quantitatively with the well-described power law of practice. Methods: Kaohsiung Medical University Hospital set up a robotic spine surgery team by the neurosurgery department in 2013 and the orthopedic department joined the well-established team in 2014. A total of consecutive 150 cases received robotic assisted spinal surgery. The 150 cases, with 841 transpedicular screws were enrolled into 3 groups: the first 50 cases performed by neurosurgeons, the first 50 cases by orthopedic surgeons, and 50 cases by neurosurgeons after the orthopedic surgeons joined the team. The time per screw and accuracy by each group and individual surgeon were analyzed. Results: The time per screw for each group was 9.56 ± 4.19, 7.29 ± 3.64, and 8.74 ± 5.77 minutes, respectively, with p-value 0.0017. The accuracy was 99.6% (253/254), 99.5% (361/363), and 99.1% (222/224), respectively, with p-value 0.77. Though the first group took time significantly more on per screw placement but without significance on the nonlinear parallelism F-test. Analysis of 5 surgeons and their first 10 cases of short segment surgery showed the time per screw by each surgeon was 12.28 ± 5.21, 6.38 ± 1.54, 8.68 ± 3.10, 6.33 ± 1.90, and 6.73 ± 1.81 minutes. The first surgeon who initiated the robotic spine surgery took significantly more time per screw, and the nonlinear parallelism test also revealed only the first surgeon had a steeper learning curve. Conclusion: This is the first study to demonstrate that differences of learning curves between individual surgeons and teams. The roles of teamwork and the unmet needs due to lack of active perception are discussed. [ABSTRACT FROM AUTHOR]