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Providing Effective Real-Time Feedback in Simulation-Based Surgical Training

  • Xingjun MaEmail author
  • Sudanthi Wijewickrema
  • Yun Zhou
  • Shuo Zhou
  • Stephen O’Leary
  • James Bailey
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

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 shows that the proposed method is able to extract highly effective feedback at a high level of efficiency.

Keywords

Real-time feedback Surgical simulation Random forests 

Notes

Acknowledgements

This research has received support from the Office of Naval Research Global.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Xingjun Ma
    • 1
    Email author
  • Sudanthi Wijewickrema
    • 1
  • Yun Zhou
    • 1
  • Shuo Zhou
    • 1
  • Stephen O’Leary
    • 1
  • James Bailey
    • 1
  1. 1.The University of MelbourneMelbourneAustralia

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