Do Skills Naturally Transfer Between Multiport and Single-Port Robotic Platforms? A Comparative Study in a Simulated Environment.

Journal: Journal of endourology
Published Date: Jan 3, 2023

Abstract

With introduction of the da Vinci single-port (SP) system, we evaluated which multiport (MP) robotic skills are naturally transferable to the SP platform. Three groups of urologists: Group 1 (5 inexperienced in MP and SP), Group 2 (5 experienced in MP without SP experience), and Group 3 (2 experienced in both MP and SP) were recruited to complete a validated urethrovesical anastomosis simulation using MP followed by SP robots. Performance was graded using both GEARS and RACE scales. Subjective cognitive load measurements (Surg-TLX and difficulty ratings [/20] of instrument collisions camera and EndoWrist movement) were collected. GEARS and RACE scores for Groups 1 and 3 were maintained on switching from MP to SP (Group 3 scored significantly higher on both systems). Surg-TLX and difficulty scores were also maintained for both groups on switching from MP and SP except for a significant increase in SP camera movement (+7.2,  = 0.03) in Group 1 compared to Group 3 that maintained low scores on both. Group 2 demonstrated significant lower GEARS (-2.9,  = 0.047) and RACE (-5.1,  = 0.011) scores on SP MP. On subanalysis, GEARS subscores for force sensitivity and robotic control (-0.7,  = 0.04; -0.9,  = 0.02) and RACE subscores for needle entry, needle driving, and tissue approximation (-0.9,  = 0.01; -1.0,  = 0.02; -1.0,  < 0.01) significantly decreased. GEARS (depth perception, bimanual dexterity, and efficiency) and RACE subscores (needle positioning and suture placement) were maintained. All participants scored significantly lower in knot tying on the SP robot (-1.0,  = 0.03; -1.2,  = 0.02, respectively). Group 2 reported higher Surg-TLX (+13 pts,  = 0.015) and difficulty ratings on SP MP (+11.8,  < 0.01; +13.6,  < 0.01; +14 pts,  < 0.01). The partial skill transference across robots raises the question regarding SP-specific training for urologists proficient in MP. Novices maintained difficulty scores and cognitive load across platforms, suggesting that concurrent SP and MP training may be preferred.

Authors

  • Ahmed Ghazi
    Department of Urology, University of Rochester Medical Center, Rochester, NY, USA.
  • Nathan Schuler
    Simulation Innovation Laboratory, Department of Urology, University of Rochester Medical Center, Rochester, New York, USA.
  • Patrick Saba
    Simulation Innovation Laboratory, Department of Urology, Transplant, University of Rochester Medical Center, Rochester, New York, USA.
  • Tyler Holler
    Simulation Innovation Laboratory, Department of Urology, University of Rochester Medical Center, Rochester, New York, USA.
  • Alexis Steinmetz
    Department of Urology, University of Rochester Medical Center, Rochester, New York, USA.
  • Kit Yuen
    Department of Urology, University of Rochester Medical Center, Rochester, New York, USA.
  • Karen Doersch
    Department of Urology, University of Rochester Medical Center, Rochester, New York, USA.
  • Elizabeth Ellis
    Department of Urology, University of Rochester Medical Center, Rochester, New York, USA.
  • William Tabayoyong
    Department of Urology, University of Rochester Medical Center, Rochester, New York, USA.
  • Jonathan Bloom
    Department of Urology, University of Rochester Medical Center, Rochester, New York, USA.
  • Hani Rashid
    Department of Urology, University of Rochester Medical Center, Rochester, New York, USA.
  • Nicholas Kavoussi
    Vanderbilt University Medical Center, Nashville, Tennessee.
  • Jean Joseph
    1 Section of Laparoscopic and Robotic Surgery, Department of Urology, University of Rochester Medical Center , Rochester, New York.

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