A high-immersive medical training platform using direct intraoperative data

Tai, Yonghang, Wei, Lei, Xiao, Minhui, Zhou, Hailing, Li, Qiong, Shi, Junsheng and Nahavandi, Saeid 2018, A high-immersive medical training platform using direct intraoperative data, IEEE access, vol. 6, pp. 69438-69452, doi: 10.1109/ACCESS.2018.2877732.

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Title A high-immersive medical training platform using direct intraoperative data
Author(s) Tai, Yonghang
Wei, LeiORCID iD for Wei, Lei orcid.org/0000-0001-8267-0283
Xiao, Minhui
Zhou, HailingORCID iD for Zhou, Hailing orcid.org/0000-0001-5009-4330
Li, Qiong
Shi, Junsheng
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name IEEE access
Volume number 6
Start page 69438
End page 69452
Total pages 15
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2018-10-31
ISSN 2169-3536
Keyword(s) Surgical training
Clinical trials
Science & Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Computer Science
Summary The virtual training of primitive surgical procedures has been widely recognized as immersive and effective to medical education. Virtual basic surgical training framework integrated with multi-sensations rendering has been recognized as one of the most immersive implementations in medical education. Yet, compared with the original intraoperative data, there has always been an argument on the lower fidelity these data are represented in virtual surgical training. In this paper, a solution is proposed to achieve better training immersion by incorporating multiple higher-fidelity factors toward a trainee's sensations (vision, touch, and hearing) during virtual training sessions. This was based on the proposal of a three-tier model to classify reasons leading to fidelity issues. This include: haptic factors, such as high-quality fitting of force models based on surgical data acquisition, the use of actual surgical instrument linked to desktop haptic devices; visual factors, such as patient-specific CT images segmentation and reconstruction from the original medical data; and hearing factors, such as variations of the sound of monitoring systems in the theatre under different surgical conditions. Twenty seven urologists comprising 18 novices and 9 professors were invited to test a virtual training system based on the proposed solution. Post-test values from both professors' and novices' groups demonstrated obvious improvements in comparison with pre-test values under both the subjective and objective criteria, the fitting rate of the whole puncture processing is 99.93%. Both the subjective and objective results demonstrated a higher performance than the existing benchmark training platform. Combining these in a systematic approach, tuned with specific fidelity requirements, haptically enabled training simulation systems would be able to provide a more immersive and effective training environment.
Language eng
DOI 10.1109/ACCESS.2018.2877732
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2018, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30115619

Document type: Journal Article
Collection: Institute for Intelligent Systems Research and Innovation (IISRI)
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