The virtual reality simulator dV-Trainer® is a valid assessment tool for robotic surgical skills
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Exponential development of minimally invasive techniques, such as robotic-assisted devices, raises the question of how to assess robotic surgery skills. Early development of virtual simulators has provided efficient tools for laparoscopic skills certification based on objective scoring, high availability, and lower cost. However, similar evaluation is lacking for robotic training. The purpose of this study was to assess several criteria, such as reliability, face, content, construct, and concurrent validity of a new virtual robotic surgery simulator.
This prospective study was conducted from December 2009 to April 2010 using three simulators dV-Trainers® (MIMIC Technologies®) and one Da Vinci S® (Intuitive Surgical®). Seventy-five subjects, divided into five groups according to their initial surgical training, were evaluated based on five representative exercises of robotic specific skills: 3D perception, clutching, visual force feedback, EndoWrist® manipulation, and camera control. Analysis was extracted from (1) questionnaires (realism and interest), (2) automatically generated data from simulators, and (3) subjective scoring by two experts of depersonalized videos of similar exercises with robot.
Face and content validity were generally considered high (77 %). Five levels of ability were clearly identified by the simulator (ANOVA; p = 0.0024). There was a strong correlation between automatic data from dV-Trainer and subjective evaluation with robot (r = 0.822). Reliability of scoring was high (r = 0.851). The most relevant criteria were time and economy of motion. The most relevant exercises were Pick and Place and Ring and Rail.
The dV-Trainer® simulator proves to be a valid tool to assess basic skills of robotic surgery.
KeywordsdV-Trainer Surgical education Da Vinci robot Reliability and validity Robotic surgery Simulation
The authors thank Ecole de Chirurgie de Nancy and its staff, CRAN (Centre de Recherche en Automatisme de Nancy) and its staff, Conseil Régional de Lorraine, Communauté Urbaine du Grand Nancy, and Association des Chefs de Service du CHU de Nancy. This work was supported by Conseil Régional de Lorraine, Communauté Urbaine du Grand Nancy, and Association des Chefs de Service du CHU de Nancy.
Cyril Perrenot, Dr. Perez, Dr. Tran, Jehl Jean-Philippe, Dr. Felblinger, Dr. Bresler, and Dr. Hubert have no conflict of interest or financial ties to disclose.