Assessing the efficacy of dissection gestures in robotic surgery.

Journal: Journal of robotic surgery
Published Date:

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.

Authors

  • Daniel A Inouye
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, University of Southern California Institute of Urology, Los Angeles, CA, USA.
  • Runzhuo Ma
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California, USA.
  • Jessica H Nguyen
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA.
  • Jasper Laca
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, University of Southern California Institute of Urology, Los Angeles, CA, USA.
  • Rafal Kocielnik
    University of Washington, WA.
  • Anima Anandkumar
    Department of Computing and Mathematical Science, California Institute of Technology, Pasadena, California; NVIDIA Corporation, Santa Clara, California.
  • Andrew J Hung
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, University of Southern California Institute of Urology, Los Angeles, California. Electronic address: Andrew.Hung@med.usc.edu.