Providing Effective Real-Time Feedback in Simulation-Based Surgical Training
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 shows that the proposed method is able to extract highly effective feedback at a high level of efficiency.
KeywordsReal-time feedback Surgical simulation Random forests
This research has received support from the Office of Naval Research Global.
- 2.Wijewickrema, S., Copson, B., Zhou, Y., Ma, X., Briggs, R., Bailey, J., Kennedy, G., O’Leary, S.: Design and Evaluation of a Virtual Reality Simulation Module for Training Advanced Temporal Bone Surgery (2017)Google Scholar
- 3.Ma, X., Wijewickrema, S., Zhou, Y., Copson, B., Bailey, J., Kennedy, G., O’Leary, S.: Simulation for Training Cochlear Implant Electrode Insertion (2017)Google Scholar
- 4.Zhou, Y., Bailey, J., Ioannou, I., Wijewickrema, S., O’Leary, S., Kennedy, G.: Pattern-based real-time feedback for a temporal bone simulator. In: Proceedings of the 19th ACM Symposium on VRST, pp. 7–16 (2013)Google Scholar
- 5.Zhou, Y., Bailey, J., Ioannou, I., Wijewickrema, S., Kennedy, G., O’Leary, S.: Constructive real time feedback for a temporal bone simulator. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8151, pp. 315–322. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40760-4_40CrossRefGoogle Scholar
- 6.Cui, Z., Chen, W., He, Y., Chen, Y.: Optimal action extraction for random forests and boosted trees. In: KDD, pp. 179–188 (2015)Google Scholar
- 7.Ma, X., Bailey, J., Wijewickrema, S., Zhou, S., Mhammedi, Z., Zhou, Y., O’Leary, S.: Adversarial generation of real-time feedback with neural networks for simulation-based training. arXiv:1703.01460 (2017)
- 9.Zhou, Y., Ioannou, I., Wijewickrema, S., Bailey, J., Kennedy, G., O’Leary, S.: Automated segmentation of surgical motion for performance analysis and feedback. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 379–386. Springer, Cham (2015). doi: 10.1007/978-3-319-24553-9_47CrossRefGoogle Scholar
- 10.Yang, Q., Yin, J., Ling, C., Pan, R.: Extracting actionable knowledge from decision trees. TKDE 19(1), 43–56 (2007)Google Scholar
- 11.Breiman, L.: Some infinity theory for predictor ensembles. Technical report, Technical Report 579, Statistics Department UCB (2000)Google Scholar