Malik Anas Ahmad
ABSTRACT
The affordance of independent learning is one of the most important advantages of computer simulators for surgical training. This advantage can get dull if the simulator does not provide the useful instructional feedback to the user and the instructor has to supervise and tutor the trainee while using the simulator. In fact the continued need of instructor feedback with most existing simulators is often cited as a primary reason for the reluctance of many medical schools to fully embrace simulator technology [Sewell 2007]. Thus the incorporation of relevant, intuitive metrics in a way that it provides a constructive feedback which facilitates independent learning is essential for the development of efficient simulators. Evaluating a trainee surgeon's performance as per trainer surgeon's desire is always a challenging problem in the development of minimal invasive surgery simulators. In this research we have proposed a novel metric for trainee surgeons' performance evaluation using machine learning algorithms.
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