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Providing effective real-time feedback in simulation-based surgical training

conference contribution
posted on 2017-01-01, 00:00 authored by Daniel Ma, S Wijewickrema, Y Zhou, S Zhou, S O’Leary, J Bailey
© Springer International Publishing AG 2017. 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.

History

Event

Medical Image Computing and Computer-Assisted Intervention. Conference (2017 : Quebec City, Quebec)

Volume

10434

Series

Lecture Notes in Computer Science

Pagination

566 - 574

Publisher

Springer

Location

Quebec City, Quebec

Place of publication

Cham, Switzerland

Start date

2017-09-11

End date

2017-09-13

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319661841

Language

eng

Publication classification

E1.1 Full written paper - refereed

Title of proceedings

MICCAI 2017 : Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2017 : 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings

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