Patricia Mattingly

A comparative analysis and guide to virtual reality robotic surgical simulators

Since the US Food and Drug Administration approved robotically assisted surgical devices for human surgery in 2000, the number of surgeries utilizing this innovative technology has risen. In 2015, approximately 650 000 robot‐assisted procedures were performed worldwide. Surgeons must be properly trained to safely transition to using such innovative technology. Multiple virtual reality robotic simulators are now commercially available for educational and training purposes. There is a need for comparative evaluations of these simulators to aid users in selecting an appropriate device for their purposes.

A model for predicting the GEARS score from virtual reality surgical simulator metrics

BackgroundSurgical education relies heavily upon simulation. Assessment tools include robotic simulator assessments and Global Evaluative Assessment of Robotic Skills (GEARS) metrics, which have been validated. Training programs use GEARS for proficiency testing; however, it requires a trained human evaluator. Due to limited time, learners are reliant on surgical simulator feedback to improve their skills. GEARS and simulator scores have been shown to be correlated but in what capacity is unknown. Our goal is to develop a model for predicting GEARS score using simulator metrics.MethodsLinear and multivariate logistic regressions were used on previously reported data by this group. Subjects performed simple (Ring and Rail 1) and complex (Suture Sponge 1) tasks on simulators, the dV-Trainer (dVT) and the da Vinci Skills Simulator (dVSS). They were scored via simulator metrics and GEARS.ResultsA linear model for each simulator and exercise showed a positive linear correlation. Equations were developed for predicting GEARS Total Score from simulator Overall Score. Next, the effects of each individual simulator metric on the GEARS Total Score for each simulator and exercise were examined. On the dVSS, Excessive Instrument Force was significant for Ring and Rail 1 and Instrument Collision was significant for Suture Sponge 1. On the dVT, Time to Complete was significant for both exercises. Once the significant variables were identified, multivariate models were generated. Comparing the predicted GEARS Total Score from the linear model (using only simulator Overall Score) to that using the multivariate model (using the significant variables for each simulator and exercise), the results were similar.ConclusionsOur results suggest that trainees can use simulator Overall Score to predict GEARS Total Score using our linear regression equations. This can improve the training process for those preparing for high-stakes assessments.