Validation of the RobotiX Mentor Robotic Surgery Simulator

Abstract

OBJECTIVES: To assess face, content, and construct validity of the RobotiX Mentor virtual reality simulator, to assess its acceptability as a robotic surgery training tool and feasibility of its use, and to develop a supplementary training curriculum. SUBJECTS AND METHODS: This prospective, observational, and comparative study recruited novice (n = 20), intermediate (n = 15), and expert (n = 11) robotic surgeons as participants from institutions across the United Kingdom and at the 30th European Association of Urology Annual Meeting. Each participant completed nine surgical tasks across two modules on the simulator, followed by a questionnaire to evaluate subjective realism (face validity), task importance (content validity), feasibility, and acceptability. Outcome measures of novice, intermediate, and expert groups were compared using Mann-Whitney U-tests to assess construct validity. RESULTS: Construct validity was demonstrated in a total of 17/25 performance evaluation metrics (p < 0.001). Experts performed better than intermediates with regard to time taken to complete the first (p = 0.002) and second (p = 0.043) module, number of instrument collisions (p = 0.040), path length (p = .049), number of cuts >2 mm deep (p = 0.033), average distance from suture target (p = 0.015), and number of suture breakages (p = 0.038). Participants determined both the simulator console and psychomotor tasks as highly realistic (mean: 3.7/5) and important for surgical training (4.5/5), with system pedals (4.2/5) and knot tying task (4.6/5) scoring highest, respectively. The simulator was also rated as an acceptable (4.3/5) tool for training and its use highly feasible (4.3/5). CONCLUSION: Construct, face, and content validity was established for the RobotiX Mentor, and feasibility and acceptability of incorporation into surgical training were ascertained. The RobotiX Mentor shows potential as a valuable tool for training and assessment of trainees in robotic skills. Investigation of concurrent and predictive validity is necessary to complete validation, and evaluation of learning curves would provide insight into its value for training.