# Sensorized Vascular High-Fidelity Physical Simulator for Robot-Assisted Surgery Training: A Multisite Pilot Evaluation

## TL;DR

This study evaluates a high-fidelity simulator for robot-assisted surgery, showing it can distinguish skill levels and is well-received by participants.

## Contribution

A sensorized vascular simulator was developed and tested for its ability to differentiate surgical expertise and specialty differences.

## Key findings

- The simulator showed discriminant validity with significant p-values for deformation measurements.
- Urologists differed significantly from general surgeons and gynecologists in performance.
- The simulator received high face- and content validity scores from participants.

## Abstract

Background/Objectives: Robot-Assisted Surgery poses challenges in skill acquisition due to the lack of haptic feedback, which may lead to adverse intraoperative events. This study focused on a multisite pilot evaluation on the simulator’s ability to discriminate between different levels of expertise and the ability to explore potential differences between surgical specialties. Methods: We built a simulator that can replicate anatomies of vascular and adipose tissue. A resistive stretching sensor was integrated into a silicone vessel to objectively measure its deformation. A total of 18 males and 12 females, aged between 26 and 64 years old, participated to the study. In total, there were 30 participants, (21 general surgeons, 2 thoracic surgeons, 4 gynecologists, 3 urologists) and they performed two repetitions of a surgical task and filled in a questionnaire about face- and content validities and a system usability scale. The tests were conducted between February and October 2023. Results: The discriminant validity was positively assessed, considering the maximum deformation value (p-value = 0.0479) and the mean deformation value (p-value = 0.0317). Differences were found between urologists, (i) general surgeons (p-value = 0.0167) and, (ii) gynecologists (p-value = 0.0495). The face- and content validity of the simulator received 80% and 90% of positive answers, respectively. Conclusions: Future works will deal with the evaluation of the simulator abilities in surgical training by comparing surgeons trained on the simulator to those who are not.