Towards validation of robotic surgery training assessment across training platforms

Abstract

Robotic surgery is increasingly popular for a wide range of complex minimally invasive surgery procedures. To improve robotic surgery training, a skills trainer simulator called dV-Trainer has recently been introduced, and a da Vinci Skills Simulator is in advanced evaluation. These platforms report a range of time and motion based task metrics and literature has investigated the validity of these metrics in training studies. However, the lack of a cross-platform data collection system has so far prevented a cross-platform investigation. Using a new architecture for collecting cross-platform motion data, we present the first study investigating whether metrics previously validated in simulation environments also hold in training exercises with a real robotic system. Preliminary experiments for an anastomosis needle throwing task in both simulated and real robotic environments are presented, and corresponding performance metrics for both proficient and trainee users are reported.