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Exploration using holographic hands as a modality for skills training in medicine
Author(s): Regina Leung; Andras Lasso; Matthew S. Holden; Boris Zevin; Gabor Fichtinger
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Paper Abstract

PURPOSE: Gaining proficiency in technical skills involving specific hand motions is prevalent across all disciplines of medicine and particularly relevant in learning surgical skills such as knot tying. We propose a new form of self-directed learning where a pair of holographic hands is projected in front of the trainee using the Microsoft HoloLens and guides them through learning various basic hand motions relevant to surgery and medicine. This study looks at the feasibility and effectiveness of using holographic hands as a skills training modality for learning hand motions compared to the traditional methods of apprenticeship and video-based learning. METHODS: 9 participants were recruited and each learned 6 different hand motions from 3 different modalities (video, apprenticeship, HoloLens). Results of successful completion and feedback on effectiveness was obtained through a questionnaire. RESULTS: Participants had a considerable preference for learning from HoloLens and apprenticeship and a higher success rate of learning hand motions compared to video-based learning. Furthermore, learning with holographic hands was shown to be comparable to apprenticeship in terms of both effectiveness and success rate. However, more participants still selected apprenticeship as a preferred learning method compared to HoloLens. CONCLUSION: This initial pilot study shows promising results for using holographic hands as a new effective form of self-directed apprenticeship learning that can be applied to learning a wide variety of skills requiring hand motions in medicine. Work continues toward implementing this technology in knot tying and suture tutoring modules in our undergraduate medical curriculum.

Paper Details

Date Published: 13 March 2018
PDF: 7 pages
Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 1057611 (13 March 2018); doi: 10.1117/12.2295495
Show Author Affiliations
Regina Leung, Queen's Univ. (Canada)
Andras Lasso, Queen's Univ. (Canada)
Matthew S. Holden, Queen's Univ. (Canada)
Boris Zevin, Queen's Univ. (Canada)
Gabor Fichtinger, Queen's Univ. (Canada)


Published in SPIE Proceedings Vol. 10576:
Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Robert J. Webster, Editor(s)

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