Learning Curve in Robotic Stereoelectroencephalography: Single Platform Experience

Taylor Niznik; Audrey Grossen; Helen Shi; Mark Stephens; Cherie Herren; Virendra R Desai

doi:10.1016/j.wneu.2023.11.119

Learning Curve in Robotic Stereoelectroencephalography: Single Platform Experience

World Neurosurg. 2024 Feb:182:e442-e452. doi: 10.1016/j.wneu.2023.11.119. Epub 2023 Nov 28.

Authors

Taylor Niznik¹, Audrey Grossen¹, Helen Shi¹, Mark Stephens¹, Cherie Herren², Virendra R Desai³

Affiliations

¹ Department 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.
² Department of Neurology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.
³ Department 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. Electronic address: Virendra-desai@ouhsc.edu.

PMID: 38030071
DOI: 10.1016/j.wneu.2023.11.119

Abstract

Background: Learning curve, training, and cost impede widespread implementation of new technology. Neurosurgical robotic technology introduces challenges to visuospatial reasoning and requires the acquisition of new fine motor skills. Studies detailing operative workflow, learning curve, and patient outcomes are needed to describe the utility and cost-effectiveness of new robotic technology.

Methods: A retrospective analysis was performed of pediatric patients who underwent robotic stereoelectroencephalography (sEEG) with the Medtronic Stealth Autoguide. Workflow, total operative time, and time per electrode were evaluated alongside target accuracy assessed via error measurements and root sum square. Patient demographics and clinical outcomes related to sEEG were also assessed.

Results: Robot-assisted sEEG was performed in 12 pediatric patients. Comparison of cases over time demonstrated a mean operative time of 363.3 ± 109.5 minutes for the first 6 cases and 256.3 ± 59.1 minutes for the second 6 cases, with reduced operative time per electrode (P = 0.037). Mean entry point error, target point error, and depth point error were 1.82 ± 0.77 mm, 2.26 ± 0.71 mm, and 1.27 ± 0.53 mm, respectively, with mean root sum square of 3.23 ± 0.97 mm. Error measurements between magnetic resonance imaging and computed tomography angiography found computed tomography angiography to be more accurate with significant differences in mean entry point error (P = 0.043) and mean target point error (P = 0.035). The epileptogenic zone was identified in 11 patients, with therapeutic surgeries following in 9 patients, of whom 78% achieved an Engel class I.

Conclusions: This study demonstrated institutional workflow evolution and learning curve for the Autoguide in pediatric sEEG, resulting in reduced operative times and increased accuracy over a small number of cases. The platform may seamlessly and quickly be incorporated into clinical practice, and the provided workflow can facilitate a smooth transition.

Keywords: Accuracy; Learning curve; Pediatric; Stealth Autoguide; Stereoelectroencephalography; Workflow; sEEG.

MeSH terms

Child
Drug Resistant Epilepsy* / diagnostic imaging
Drug Resistant Epilepsy* / surgery
Electrodes, Implanted
Electroencephalography / methods
Humans
Learning Curve
Retrospective Studies
Robotic Surgical Procedures* / methods
Robotics*
Stereotaxic Techniques