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Learning curves in robotic neurosurgery: a systematic review
Nathan A Shlobin1, Jonathan Huang2, Chengyuan Wu3
1Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, 676 N. St. Clair Street, Suite 2210, Chicago, IL, 60611, USA. nathan.shlobin@northwestern.edu.
Neurosurgical Review
|December 12, 2022
Summary
Robotic surgery in neurosurgery involves a learning curve, but studies show varied parameters and duration. Future research should aim to shorten the learning curve for robotic procedures in neurosurgery.
Area of Science:
- Neurosurgery
- Robotic Surgery
- Medical Technology
Background:
- The adoption of robotic surgery necessitates a learning period for surgeons to adapt to new technologies.
- A comprehensive understanding of robotic learning curves across neurosurgery subspecialties is lacking.
- This study systematically reviews the existing literature on robotic learning curves in neurosurgery.
Approach:
- A systematic literature search was conducted across PubMed, Embase, and Scopus databases.
- Articles were screened by title, abstract, and full text for relevance and data extraction.
- 32 articles encompassing 3074 patients were included, focusing on spine, pediatric, functional, and general neurosurgery.
Key Points:
- Learning curves were identified in robotic neurosurgery, but assessed parameters were highly heterogeneous.
- While some studies showed reduced operative times with increased case volume, others found no significant learning curve.
- Accuracy improvements over time were reported in 44.4% of studies, with learning curve durations ranging from 3 to 75 cases.
Conclusions:
- Robotic neurosurgery exhibits common learning curves, characterized by significant heterogeneity in parameters and duration.
- The variability in learning curve assessment hinders consistent interpretation and application.
- Future research should focus on developing strategies to reduce the number of cases required to achieve proficiency in robotic neurosurgery.
