Multicentric study to validate a sensorized vascular high-fidelity physical simulator through different surgical experiences and specialties

Robot-assisted surgery (RAS) involves mastering surgical instruments facing the absence of haptic feedback, thus prolonging the learning curves [1]. Applying excessive force to delicate tissues, such as blood vessels, can lead to serious intraoperative events like bleeding [2], causing conversion to open surgery in emergency. Surgeons need to develop skills to visually determine the tissue deformation – so called visual haptic - and hand-eye coordination [3]. Training is crucial to prevent such adverse occurrences, and simulation has become a key method for acquiring technical skills. Surgical simulators should be used to train surgeons, avoiding operation on patients in the learning stage. Starting from a developed physical simulator [4], the aim of this study is to assess (i) the construct validity - ability of discriminate between different levels of expertise - of the physical high-fidelity vascular simulator equipped with soft strain sensor measuring the vessel deformation during a simulated task, and (ii) the ability of the proposed system of distinguishing between different surgical specialties in novices.