Robot-assisted versus navigation-assisted screw placement in spinal vertebrae

Comparison of Rostral Facet Joint Violations in Robotic- and Navigation-Assisted Pedicle Screw Placement for Adult Lumbar Spine Instrumentation

BACKGROUND CONTEXT

Facet joint violation by pedicle screws may lead to adjacent-segment disease and postoperative pain. Previous studies have reported the incidence of rostral facet joint violation using various pedicle screw insertion techniques. However, the incidence of facet joint violations with robotic guidance has not been determined.

PURPOSE

To investigate and compare the incidence of rostral facet joint violation by pedicle screws under robotic guidance and computerized tomography (CT) navigation guidance.

STUDY DESIGN/SETTING

Retrospective Matched Cohort PATIENT SAMPLE: All patients who underwent robotic-assisted lumbar fusion at a major spine center up until 2023 were retrospectively identified and matched 1:3 to patients undergoing CT navigation guidance based by on age, sex, rostral vertebral level, and length of construct. Inclusion criteria consisted of age greater than 18 years, bilateral pedicle screw fixation, and presence of a postoperative CT scan of the lumbar spine or abdomen/pelvis at any point in the post-operative period.

OUTCOME MEASURES

Rostral facet joint violations METHODS: Descriptive statistics were used to compare cohorts: frequencies, chi-squared analysis for categorical variables, and t-test for continuous variables.

RESULTS

A total of 408 rostral pedicle screws were implanted in 204 patients (Robot: 102; Navigation: 306). Overall, 13 (12.3%) rostral facet joint violations were observed in the robot cohort and 75 (24.5%) in the navigation cohort (p=0.01). Specifically, fewer robotic violations were observed at the L2 (3.5% vs 32.1%, p=0.003) and L3 levels (3.9% vs. 18.1%, p=0.08) compared to navigation. No difference was observed at L4 and L5. Bilateral violations are significantly reduced with robotic approaches (5.3% vs. 14.4%, p=0.03). Lastly, more facet joint violations were observed during open approaches (robot: 18.8%, navigation: 27.3%) than percutaneous approaches (robot: 11.6%, navigation: 7.1%) in both groups (p<0.001). The rate of L4 facet violations was 18.8% in the robotic cohort and 27.3% in the navigation cohort. The rate of L5 facet violations was 31.3% in the robotic cohort and 29.2% in the navigation cohort.

CONCLUSIONS

Use of robotic assistance in lumbar pedicle screws significantly reduced the rate of rostral facet joint violations compared to navigation guidance at L2 and L3 levels, but not at L4 and L5, with facet violations approaching nearly one-third of the patients at L5 screws. Rostral facet violations can play a significant role in adjacent segment degeneration and disease. Technical factors and trajectory issues likely play a role and addressing these components should minimize unintended facet violation and proximal adding on.

Robot-assisted anterior cruciate ligament reconstruction based on three-dimensional images

Background Tunnel placement is a key step in anterior cruciate ligament (ACL) reconstruction. The purpose of this study was to evaluate the accuracy of bone tunnel drilling in arthroscopic ACL reconstruction assisted by a three-dimensional (3D) image-based robot system. Methods Robot-assisted ACL reconstruction was performed on twelve freshly frozen knee specimens. During the operation, three-dimensional images were used for ACL bone tunnel planning, and the robotic arm was used for navigation and drilling. Twelve patients who underwent traditional arthroscopic ACL reconstruction were included. 3D computed tomography was used to measure the actual position of the ACL bone tunnel and to evaluate the accuracy of the robotic and traditional ACL bone tunnel. Results On the femoral side, the positions of robotic and traditional surgery tunnels were 29.3 ± 1.4% and 32.1 ± 3.9% in the deep-to-shallow direction of the lateral femoral condyle (p = 0.032), and 34.6 ± 1.2% and 21.2 ± 9.4% in the high-to-low direction (p < 0.001), respectively. On the tibial side, the positions of the robotic and traditional surgical tunnels were located at 48.4 ± 0.9% and 45.8 ± 2.8% of the medial-to-lateral diameter of the tibial plateau (p = 0.008), 38.1 ± 0.8% and 34.6 ± 6.0% of the anterior-to-posterior diameter (p = 0.071), respectively. Conclusions In this study, ACL reconstruction was completed with the assistance of a robot arm and 3D images, and the robot was able to drill the bone tunnel more accurately than the traditional arthroscopic ACL reconstruction.

Machine Learning in Spine Surgery: A Narrative Review

Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.