Design and utilisation of a novel, high-fidelity, low-cost, hybrid-tissue simulation model to facilitate training in robot-assisted partial nephrectomy

Abstract

Robot-assisted partial nephrectomy (RAPN) has rapidly evolved as the standard of care for appropriately selected renal tumours, offering key patient benefits over radical nephrectomy or open surgical approaches. Accordingly, RAPN is a key competency that urology trainees wishing to treat kidney cancer must master. Training in robotic surgery is subject to numerous challenges, and simulation has been established as valuable step in the robotic learning curve. However, simulation models are often both expensive and suboptimal in fidelity. This means that the number of practice repetitions for a trainee may limited by cost restraints, and that trainees may struggle to reconcile the skills obtained in the simulation laboratory with real-world practice in the operating room. We have developed a high-fidelity, low-cost, customizable model for RAPN simulation based on porcine tissue. The model has been utilised in teaching courses at our institution, confirming both feasibility of use and high user acceptability. We share the design of our model in this proof-of-concept report.