Providing Effective Real-time Feedback in Simulation-based Surgical Training

TL;DR

This paper introduces a random forest-based method for providing effective, real-time automated feedback in surgical simulation training, balancing accuracy and efficiency to enhance learning outcomes.

Contribution

It presents a novel approach that improves the effectiveness and efficiency of automated feedback in simulation-based surgical training.

Findings

High-quality feedback achieved in real-time

Balanced effectiveness and efficiency demonstrated

Applicable to surgical training simulations

Abstract

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.