Robotic Arm Alignment System for Electrode Implantation in Invasive Epilepsy Monitoring: Clinical Experience and Accuracy in a Consecutive Patient Series with Comparison to the Literature

Objectives: Robot-assisted stereoelectroencephalography (SEEG) is increasingly used for invasive presurgical evaluation in drug-resistant epilepsy. Feasibility and accuracy data for robotic alignment systems remain limited. We analyzed our initial institutional experience and compared the results with the contemporary literature. Methods: We retrospectively analyzed nine consecutive patients who underwent SEEG depth electrode implantation using the Brainlab Cirq robotic alignment system and intraoperative MRI for registration. Demographic and operative variables were extracted from institutional records. Implantation accuracy was measured on fused postoperative imaging as Euclidean entry point error (EPE) and target point error (TPE). A PubMed-focused literature screen was performed to identify studies reporting robotic SEEG implantation accuracy and/or efficiency metrics for comparison. Results: Nine patients underwent implantation of 69 SEEG electrodes. Mean age was 21.7 ± 11.4 years. A mean of 7.7 ± 2.1 electrodes was implanted per patient. Mean operative time was 120.2 ± 31.9 min, corresponding to 16.0 ± 3.4 min per electrode. Mean EPE was 1.36 ± 0.60 mm and mean TPE was 1.79 ± 0.69 mm. Moreover, the maximum projected skull-entry angle was not independently associated with either EPE or TPE. In comparison with published robotic SEEG series and meta-analyses, our accuracy results fall within the established high-precision range and compare favorably with the most recent robotic-arm-specific data. Conclusions: In this initial consecutive series, our robotic arm alignment system enabled accurate SEEG implantation with sub-2-mm mean target error. These findings support robotic arm systems as effective robotic alignment platforms for invasive epilepsy monitoring, while larger multicenter studies remain necessary to define comparative accuracy, learning curve effects, and complication profiles.