Related Experiment Video
Updated: Jul 9, 2025
Robotic-Guided Stereoelectroencephalography for Invasive Epilepsy Monitoring
Published on: June 13, 2025
Learning Curve in Robotic Stereoelectroencephalography: Single Platform Experience.
Taylor Niznik1, Audrey Grossen1, Helen Shi1
1Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA; Department of Neurosurgery, Section of Pediatric Neurosurgery, Oklahoma Children's Hospital, University of Oklahoma School of Medicine, Oklahoma City, Oklahoma, USA.
This study shows that using robotic assistance for pediatric stereoelectroencephalography (sEEG) reduces operative time and improves accuracy as surgeons gain experience. The Autoguide system can be integrated into clinical practice to streamline workflows.
Area of Science:
- Neurosurgery
- Medical Technology
- Pediatric Neurology
Background:
- Widespread adoption of new technology in neurosurgery is hindered by learning curves, training requirements, and costs.
- Neurosurgical robotic technology presents unique challenges in visuospatial reasoning and fine motor skill acquisition.
- There is a need for studies on operative workflow, learning curves, and patient outcomes to assess the utility and cost-effectiveness of robotic technology.
Purpose of the Study:
- To evaluate the learning curve and workflow efficiency of robotic-assisted stereoelectroencephalography (sEEG) in pediatric patients.
- To assess the accuracy and clinical outcomes of robotic-assisted sEEG using the Medtronic Stealth Autoguide system.
- To determine the feasibility of integrating robotic technology into pediatric neurosurgical practice.
Main Methods:
- A retrospective analysis of 12 pediatric patients who underwent robotic-assisted sEEG with the Medtronic Stealth Autoguide.
- Evaluation of operative workflow, total operative time, and time per electrode.
- Assessment of target accuracy using error measurements (entry point, target point, depth point) and root sum square (RSS).
- Comparison of imaging modalities (MRI vs. CT angiography) for accuracy.
Main Results:
- Operative time decreased significantly from the first 6 to the second 6 cases (363.3 min vs. 256.3 min, P=0.037).
- Mean errors were: entry point 1.82±0.77 mm, target point 2.26±0.71 mm, depth point 1.27±0.53 mm, with a mean RSS of 3.23±0.97 mm.
- CT angiography demonstrated higher accuracy than MRI, with significant differences in entry point (P=0.043) and target point (P=0.035) errors.
- The epileptogenic zone was identified in 11 patients, leading to surgery in 9, with 78% achieving Engel class I outcome.
Conclusions:
- Institutional workflow and the learning curve for robotic-assisted pediatric sEEG using the Autoguide system evolved, leading to reduced operative times and improved accuracy.
- The robotic platform can be efficiently integrated into clinical practice.
- The established workflow facilitates a smooth transition for robotic-assisted sEEG procedures in pediatric patients.
