• Christopher Sewell, Dan Morris, Nikolas H Blevins, Sanjeev Dutta, Sumit Agrawal, Federico Barbagli, and Kenneth Salisbury.
• Department of Computer Science, Stanford University, Stanford, California, USA. csewell@cs.stanford.edu
• Comput. Aided Surg. 2008 Mar 1; 13 (2): 63-81.

AbstractOne of the most important advantages of computer simulators for surgical training is the opportunity they afford for independent learning. However, if the simulator does not provide useful instructional feedback to the user, this advantage is significantly blunted by the need for an instructor to supervise and tutor the trainee while using the simulator. Thus, the incorporation of relevant, intuitive metrics is essential to the development of efficient simulators. Equally as important is the presentation of such metrics to the user in such a way so as to provide constructive feedback that facilitates independent learning and improvement. This paper presents a number of novel metrics for the automated evaluation of surgical technique. The general approach was to take criteria that are intuitive to surgeons and develop ways to quantify them in a simulator. Although many of the concepts behind these metrics have wide application throughout surgery, they have been implemented specifically in the context of a simulation of mastoidectomy. First, the visuohaptic simulator itself is described, followed by the details of a wide variety of metrics designed to assess the user's performance. We present mechanisms for presenting visualizations and other feedback based on these metrics during a virtual procedure. We further describe a novel performance evaluation console that displays metric-based information during an automated debriefing session. Finally, the results of several user studies are reported, providing some preliminary validation of the simulator, the metrics, and the feedback mechanisms. Several machine learning algorithms, including Hidden Markov Models and a Naïve Bayes Classifier, are applied to our simulator data to automatically differentiate users' expertise levels.

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