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Assessing the efficacy of dissection gestures in robotic surgery

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Abstract

Our group previously defined a dissection gesture classification system that deconstructs robotic tissue dissection into its most elemental yet meaningful movements. The purpose of this study was to expand upon this framework by adding an assessment of gesture efficacy (ineffective, effective, or erroneous) and analyze dissection patterns between groups of surgeons of varying experience. We defined three possible gesture efficacies as ineffective (no meaningful effect on the tissue), effective (intended effect on the tissue), and erroneous (unintended disruption of the tissue). Novices (0 prior robotic cases), intermediates (1–99 cases), and experts (≥ 100 cases) completed a robotic dissection task in a dry-lab training environment. Video recordings were reviewed to classify each gesture and determine its efficacy, then dissection patterns between groups were analyzed. 23 participants completed the task, with 9 novices, 8 intermediates with median caseload 60 (IQR 41–80), and 6 experts with median caseload 525 (IQR 413–900). For gesture selection, we found increasing experience associated with increasing proportion of overall dissection gestures (p = 0.009) and decreasing proportion of retraction gestures (p = 0.009). For gesture efficacy, novices performed the greatest proportion of ineffective gestures (9.8%, p < 0.001), intermediates commit the greatest proportion of erroneous gestures (26.8%, p < 0.001), and the three groups performed similar proportions of overall effective gestures, though experts performed the greatest proportion of effective retraction gestures (85.6%, p < 0.001). Between groups of experience, we found significant differences in gesture selection and gesture efficacy. These relationships may provide insight into further improving surgical training.

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Acknowledgements

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The authors declare that no support, financial or otherwise, were received for the preparation of this manuscript.

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Authors and Affiliations

  1. Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, University of Southern California Institute of Urology, Los Angeles, CA, USA

    Daniel A. Inouye, Runzhuo Ma, Jessica H. Nguyen, Jasper Laca & Andrew J. Hung

  2. Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA

    Rafal Kocielnik & Anima Anandkumar

Authors
  1. Daniel A. Inouye
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  2. Runzhuo Ma
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  3. Jessica H. Nguyen
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  4. Jasper Laca
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  5. Rafal Kocielnik
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  6. Anima Anandkumar
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  7. Andrew J. Hung
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Contributions

D Inouye: project development, data collection and management, data analysis, manuscript writing and editing. R Ma: project development, data management, data analysis, manuscript writing and editing. J Nguyen: project development, data analysis, manuscript writing and editing. J Laca: project development, data analysis. R Kocielnik: project development, data analysis. A Anandkumar: project development. A Hung: project development, data analysis, manuscript writing and editing.

Corresponding author

Correspondence to Andrew J. Hung.

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Conflict of interest

Andrew J. Hung has financial disclosures with Intuitive Surgical, Inc.

Ethics approval

This study was approved by the University of Southern California’s Institutional Review Board (protocol HS-16–00,318).

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Informed consent was obtained per Institutional Review Board protocol.

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Inouye, D.A., Ma, R., Nguyen, J.H. et al. Assessing the efficacy of dissection gestures in robotic surgery. J Robotic Surg 17, 597–603 (2023). https://doi.org/10.1007/s11701-022-01458-x

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  • DOI: https://doi.org/10.1007/s11701-022-01458-x

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