Comparative Meta-Analysis of Robot-Assisted vs Frame-Based sEEG in Drug-Resistant Epilepsy

Overview

This meta-analysis compares robot-assisted stereoelectroencephalography (sEEG) with traditional frame-based methods in patients with drug-resistant epilepsy, focusing on accuracy, procedural efficiency, and safety. Robot-assisted systems demonstrated comparable or superior accuracy, reduced operative times, and similar or lower complication rates compared to frame-based stereotaxy.

Background

Epilepsy affects millions worldwide, with a significant subset of patients exhibiting drug-resistant seizures requiring surgical intervention. Accurate localization of the epileptogenic zone (EZ) is critical for successful epilepsy surgery, often necessitating invasive sEEG electrode implantation. Traditional frame-based stereotaxy has been the gold standard for electrode placement but is limited by trajectory constraints and procedural complexity. Recent advances in robotic-assisted systems offer enhanced planning flexibility, improved workflow efficiency, and potentially greater accuracy and safety in sEEG electrode implantation.

Data Highlights

Key Findings

• Robot-assisted sEEG provides submillimetric accuracy comparable to or exceeding frame-based stereotaxy.
• RAS allows flexible, nonlinear electrode trajectories unrestricted by frame constraints.
• Procedural efficiency is improved with robot assistance, showing reduced operative times overall and per electrode.
• Complication rates, including intracranial hemorrhage, are similar or lower with robot-assisted implantation.
• Robot-assisted systems facilitate 3D trajectory planning and have expanded accessibility of sEEG in centers lacking traditional frame-based equipment.

Clinical Implications

Robot-assisted sEEG implantation offers clinicians enhanced precision and efficiency without compromising safety, supporting its adoption as a preferred technique in epilepsy surgery centers. The flexibility in trajectory planning may improve electrode targeting in complex brain regions, potentially increasing the success rate of EZ localization. Institutions should consider integrating robotic systems to optimize surgical workflow and patient outcomes in drug-resistant epilepsy management.

Conclusion

Robot-assisted sEEG represents a significant advancement over traditional frame-based methods by combining high accuracy, improved procedural efficiency, and a favorable safety profile. This meta-analysis supports the growing clinical adoption of robotic assistance in stereotactic epilepsy surgery.

References

1. Talairach et al. 1950s -- Introduction of sEEG
2. PRISMA Guidelines and Cochrane Handbook 2025 -- Systematic Review Methodology
3. Medtech/Zimmer Biomet ROSA and Renishaw Neuromate -- Robot-Assisted Systems
4. Epidemiology of Epilepsy in China and US -- Prevalence Data