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Constructive Real Time Feedback for a Temporal Bone Simulator
Yun Zhou1, James Bailey1, Ioanna Ioannou2, Sudanthi Wijewickrema2, Gregor Kennedy3, and Stephen O’Leary2
1Department of Computing and Information Systems, University of Melbourne, Australia
2Department of Otolaryngology, University of Melbourne, Australia
3Centre for the Study of Higher Education, University of Melbourne, Australia
Abstract. 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.
Keywords: real time feedback, surgical simulation, random forest
LNCS 8151, p. 315 ff.
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© Springer-Verlag Berlin Heidelberg 2013