Stereo-electroencephalography (SEEG) Surgery Evolution from Mind to Robot

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Abstract Description

Introduction

Stereo-Electroencephalography (SEEG) has become a cornerstone in the presurgical evaluation of drug-resistant epilepsy cases, offering precise intracranial monitoring with minimal invasiveness compared to traditional invasive subdural and depth electrodes implantation in previous era.  This state-of-art is nicely matching with our advancements in neuro-navigation system, and the evolution is enhanced with the introduction of neurosurgical robotic system resulting in accuracy and efficiency refinement of SEEG electrode implantation and the extended therapeutic intervention.  This presentation reviews the evolution of SEEG technology in epilepsy surgery at Queen Elizabeth Hospital, comparing outcomes between frameless and robotic assisted systems across 12 cases.


Method

We retrospectively analyzed 12 consecutive SEEG operations performed at our department, comprising 7 cases utilizing frameless neuro-navigation system and 5 cases employing neurosurgical robotic system.  Key metrics evaluated include:

1.    Operational efficiency

2.    Accuracy

3.    Clinical outcomes

4.    Technical challenges and solutions


Result

12 SEEG operations were performed from November 2020 to February 2025 in 11 patients.  They consisted of 7 male and 4 female patients with median age of 36 (24-49).  Preliminary data suggest that SEEG had superior advantages compared with traditional invasive EEG electrodes implantation.  Robotic-assisted SEEG implantation offer superior entry and target precision and reduced procedural time compared to frameless techniques.  Insignificant complication rate were achieved with comparable postoperative patient outcomes and seizure control rate.


Conclusion

The SEEG is an evolving and safe surgical tool in both diagnostic and therapeutic application in epilepsy surgery.  Integration of neurosurgical robotic system into SEEG workflows represents a significant advancement in enhancing precision and scalability.

Abstract ID :
HAC1218
Submission Type
Chief of Service and Consultant Neurosurgeon
,
Queen Elizabeth Hospital

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