Providing Effective Real-time Feedback in Simulation-based Surgical Training
This addresses the need for better training tools in surgical education by improving feedback mechanisms, though it is incremental as it builds on existing data mining techniques.
The paper tackles the problem of providing automated real-time performance feedback in surgical training simulations, proposing a random forest-based method that achieves a balance between effectiveness and efficiency, as demonstrated in a temporal bone surgery simulation.
Virtual reality simulation is becoming popular as a training platform in surgical education. However, one important aspect of simulation-based surgical training that has not received much attention is the provision of automated real-time performance feedback to support the learning process. Performance feedback is actionable advice that improves novice behaviour. In simulation, automated feedback is typically extracted from prediction models trained using data mining techniques. Existing techniques suffer from either low effectiveness or low efficiency resulting in their inability to be used in real-time. In this paper, we propose a random forest based method that finds a balance between effectiveness and efficiency. Experimental results in a temporal bone surgery simulation show that the proposed method is able to extract highly effective feedback at a high level of efficiency.