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Learning Curves during Implementation of Robotic Stereotactic Surgery.

Kevin Hines1, Rupert D Smit1, Shreya Vinjamuri1

  • 1Department of Neurological Surgery, Thomas Jefferson University and Jefferson Hospital for Neuroscience, Philadelphia, Pennsylvania, USA.

Stereotactic and Functional Neurosurgery
|May 12, 2024
PubMed
Summary

This study used cumulative sum (CUSUM) analysis to quantify the learning curve for robotic stereoelectroencephalography (sEEG) and deep brain stimulation (DBS) surgeries. Surgeons reached a learning plateau in robotic cranial procedures, providing insights for integrating new technology.

Keywords:
Deep brain stimulationLearning curveRoboticsStereoelectroencephalographyStereotactic surgery

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Area of Science:

  • Neurosurgery
  • Robotics
  • Surgical Education

Background:

  • Robotic techniques are increasingly adopted in neurosurgery for procedures like stereoelectroencephalography (sEEG) and deep brain stimulation (DBS).
  • Understanding surgeon learning curves is crucial for integrating new robotic technologies into surgical workflows.
  • Cumulative sum (CUSUM) analysis is a method for quantifying learning curves in surgery.

Purpose of the Study:

  • To analyze the learning curve associated with robotic-assisted stereotactic procedures, specifically deep brain stimulation (DBS) and stereoelectroencephalography (sEEG).
  • To quantify operative times using CUSUM analysis for robotic DBS and sEEG cases.
  • To provide data on surgeon expectations when integrating robotic cranial applications.

Main Methods:

  • Retrospective review of robotic stereotactic cases at a single institution.
  • Application of CUSUM analysis to operative times for two surgeons.
  • Inclusion of 273 cases: 188 for DBS and 85 for sEEG.

Main Results:

  • Demonstrated learning phase durations of 20 cases for DBS and 16 cases for sEEG.
  • Mastery phases began at case 132 for DBS and case 72 for sEEG after operative time plateaued.
  • Observed a learning plateau correlating with a change in surgical location after the learning phase.

Conclusions:

  • This study presents the learning curve for two stereotactic workflows integrating robotics.
  • It is the first study to examine the robotic learning curve in DBS using CUSUM analysis.
  • The findings offer valuable data for surgeons integrating robotic technology in cranial neurosurgery.