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Learning curves in robotic neurosurgery: a systematic review

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Abstract 

The transition to performing procedures robotically generally entails a period of adjustment known as a learning curve as the surgeon develops a familiarity with the technology. However, no study has comprehensively examined robotic learning curves across the field of neurosurgery. We conducted a systematic review to characterize the scope of literature on robotic learning curves in neurosurgery, assess operative parameters that may involve a learning curve, and delineate areas for future investigation. PubMed, Embase, and Scopus were searched. Following deduplication, articles were screened by title and abstract for relevance. Remaining articles were screened via full text for final inclusion. Bibliographic and learning curve data were extracted. Of 746 resultant articles, 32 articles describing 3074 patients were included, of which 23 (71.9%) examined spine, 4 (12.5%) pediatric, 4 (12.5%) functional, and 1 (3.1%) general neurosurgery. The parameters assessed for learning curves were heterogeneous. In total, 8 (57.1%) of 14 studies found reduced operative time with increased cases, while the remainder demonstrated no learning curve. Six (60.0%) of 10 studies reported reduced operative time per component with increased cases, while the remainder indicated no learning curve. Radiation time, radiation time per component, robot time, registration time, setup time, and radiation dose were assessed by ≤ 4 studies each, with 0–66.7% of studies demonstrated a learning curve. Four (44.4%) of 9 studies on accuracy showed improvement over time, while the others indicated no improvement over time. The number of cases required to reverse the learning curve ranged from 3 to 75. Learning curves are common in robotic neurosurgery. However, existing studies demonstrate high heterogeneity in assessed parameters and the number of cases that comprise the learning curve. Future studies should seek to develop strategies to reduce the number of cases required to reach the learning curve.

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Authors and Affiliations

  1. Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, 676 N. St. Clair Street, Suite 2210, Chicago, IL, 60611, USA

    Nathan A. Shlobin & Jonathan Huang

  2. Department of Neurological Surgery, Thomas Jefferson University Hospitals, Philadelphia, PA, USA

    Chengyuan Wu

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  1. Nathan A. Shlobin
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  2. Jonathan Huang
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NAS conceptualized the idea, conducted the screening and data extraction, prepared figures and tables, and wrote the manuscript draft. JH conducted the screening and data extraction and assisted in revising the manuscript. CW conceptualized the idea, assisted in revising the manuscript, and provided project supervision. All authors reviewed and approved the submitted manuscript.

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Correspondence to Nathan A. Shlobin.

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Shlobin, N.A., Huang, J. & Wu, C. Learning curves in robotic neurosurgery: a systematic review. Neurosurg Rev 46, 14 (2023). https://doi.org/10.1007/s10143-022-01908-y

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