To address this, we aimed to explore the use of our validated assessment tool End-To-End Assessment of Suturing Expertise (EASE) designed for live surgery to assess surgeons in a VR simulation environment while correlating their skills to live surgery. We also aimed to explore the association between specific suturing technical skills and continence recovery after RARP. Our goal was to highlight the possible utility of implementing VR training into surgical education.
The NIH funded study involved a total of 46 participants, including 10 novices, 10 intermediate surgeons, and 26 expert surgeons who completed a VR suturing exercise on the Surgical Science VR simulator from 5- different institutions. Intermediate and expert surgeons also supplied live surgical video of the VUA step of a RARP which was analyzed using EASE. Clinical outcomes(continence recovery) were gathered for the cases provided by expert surgeons.
We found that EASE as a tool was able to distinguish the skill levels of the 3 different cohorts in a virtual training space which interestingly may point towards potential areas of improvement for training surgeons. We also found that there was a stronger correlation between skills in VR and live surgery for intermediate surgeons vs expert surgeons. We believe that this may be due to the fact that expert surgeons likely had a level of cognitive dissonance when performing on the VR platform. Of note, we did find a positive significant association between certain suturing subskills for expert surgeons on a VR platform with 3-month continence recovery. We do not endorse that suturing is the cause of improved 3-month continence recovery rates but rather that the suturing is a robust measure of the surgeon’s overall skill which likely contributed to the improvement in clinical outcomes.
This study highlights the utility of our assessment tool (EASE) in identifying potential skills training surgeons can improve upon while also supporting the utility of integration of VR training into surgical education. Our group has also focused on the implementation of artificial intelligence for skill assessment with the goal of providing rapid, personalized feedback. By incorporating AI, we can rapidly analyze vast amounts of surgical data both from virtual simulations and live surgery to identify patterns and trends which can then be incorporated into training curricula to facilitate the acquisition of skills for training surgeons. As AI continues to evolve and adapt, we aim to remain at the forefront of the integration of this powerful tool through our work to evolve and improve surgical education.
Written by: Timothy N Chu,1 Elyssa Y Wong,1 Runzhuo Ma,1 Cherine H Yang,1 Istabraq S Dalieh,1 Alvin Hui,1 Oscar Gomez,1 Steven Cen,2 Ahmed Ghazi,3 Brian J Miles,4 Clayton Lau,5 John W Davis,6 Mitchell G Goldenberg,1 Andrew J Hung7
- Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.
- Department of Radiology, University of Southern California, Los Angeles, CA, USA.
- Department of Urology, University of Rochester, Rochester, NY, USA.
- Department of Urology, Houston Methodist, Houston, TX, USA.
- Department of Urology, City of Hope, Duarte, CA, USA.
- Department of Urology, MD Anderson Cancer Center, Houston, TX, USA.
- Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.
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