Approximation of the uncertainty point-cloud for monocular knee joint measurement
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Description
During knee arthroscopy, the surgeon is under significant pressure to guide the surgical instrument through the knee joint without damaging the joint internal structures. To alleviate the burden on surgeons, this study develops a novel real-time algorithm to quantify the uncertainty of internal knee gap measurements – a key requirement toward autonomous knee arthroscopy.
We demonstrate through cadaveric experiments, the measurement and uncertainty quantification of the knee gap in real-time. Through approximating of our previous point cloud method to analyse the knee gap measurement uncertainty, the computational time is significantly reduced from greater than two hours (mainly to calculate the range of the instrument gap) to 31 milliseconds. Additionally, we show that there is a minimal decrease in accuracy of 0.19mm between the point cloud and the approximation techniques.
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| ID Code: | 231300 | ||||||
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| Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||||||
| Series Name: | Australasian Conference on Robotics and Automation, ACRA | ||||||
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| Additional Information: | Funding Information: The authors acknowledge the Advanced Queensland Scheme and the Queensland University of Technology Research Training Program funding support. Cadaveric experiments were approved by the Aus- tralian National Health and Medical Research Council (NHMRC) - Approval no. 1400000856. | ||||||
| Measurements or Duration: | 10 pages | ||||||
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| ISBN: | 978-1-7138-3068-9 | ||||||
| Pure ID: | 110357354 | ||||||
| Divisions: | Current > Research Centres > Centre for Robotics Current > Research Centres > Centre for Biomedical Technologies ?? 1479430 ?? Past > QUT Faculties & Divisions > Faculty of Health Past > QUT Faculties & Divisions > Science & Engineering Faculty Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Electrical Engineering & Robotics Current > Schools > School of Mechanical, Medical & Process Engineering |
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| Funding Information: | The authors acknowledge the Advanced Queensland Scheme and the Queensland University of Technology Research Training Program funding support. | ||||||
| Copyright Owner: | 2020 Australasian Robotics and Automation Association | ||||||
| Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||||
| Deposited On: | 23 May 2022 03:46 | ||||||
| Last Modified: | 29 Feb 2024 15:20 |
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