Constructive real time feedback for a temporal bone simulator.

As demands on surgical training efficiency increase, there is a stronger need for computer assisted surgical training systems. The ability to provide automated performance feedback and assessment is a critical aspect of such systems. The development of feedback and assessment models will allow the use of surgical simulators as self-guided training systems that act like expert trainers and guide trainees towards improved performance. This paper presents an approach based on Random Forest models to analyse data recorded during surgery using a virtual reality temporal bone simulator and generate meaningful automated real-time performance feedback. The training dataset consisted of 27 temporal bone simulation runs composed of 16 expert runs provided by 7 different experts and 11 trainee runs provided by 6 trainees. We demonstrate how Random Forest models can be used to predict surgical expertise and deliver feedback that improves trainees' surgical technique. We illustrate the potential of the approach through a feasibility study.