Mazomenos, EB orcid.org/0000-0003-0357-5996, Vasconcelos, F, Smelt, J et al. (6 more authors) (2016) Motion-Based Technical Skills Assessment in Transoesophageal Echocardiography. In: Lecture Notes in Computer Science. 7th International Conference, Medical Imaging and Augmented Reality (MIAR) 2016, 24-26 Aug 2016, Bern, Switzerland. Springer Nature , pp. 96-103. ISBN 9783319437743
Abstract
This paper presents a novel approach for evaluating technical skills in Transoesophageal Echocardiography (TEE). Our core assumption is that operational competency can be objectively expressed by specific motion-based measures. TEE experiments were carried out with an augmented reality simulation platform involving both novice trainees and expert radiologists. Probe motion data were collected and used to formulate various kinematic parameters. Subsequent analysis showed that statistically significant differences exist among the two groups for the majority of the metrics investigated. Experts exhibited lower completion times and higher average velocity and acceleration, attributed to their refined ability for efficient and economical probe manipulation. In addition, their navigation pattern is characterised by increased smoothness and fluidity, evaluated through the measures of dimensionless jerk and spectral arc length. Utilised as inputs to well-known clustering algorithms, the derived metrics are capable of discriminating experience levels with high accuracy (>84 %).
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer International Publishing Switzerland 2016. This is an author produced version of a conference paper published in Lecture Notes in Computer Science. The final authenticated version is available online at https://doi.org/10.1007/978-3-319-43775-0_9. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Skill assessment Motion analysis Transoesophageal echocardiography |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Jun 2020 10:01 |
Last Modified: | 09 Jun 2020 12:10 |
Status: | Published |
Publisher: | Springer Nature |
Identification Number: | 10.1007/978-3-319-43775-0_9 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160485 |