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Neural networks for analysis of trabecular bone in osteoarthritis
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Khovanova, N. A., Shaikhina, Torgyn and Mallick, Kajal (2014) Neural networks for analysis of trabecular bone in osteoarthritis. Bioinspired, Biomimetic and Nanobiomaterials . pp. 1-11. doi:10.1680/bbn.14.00006 ISSN 2045-9858.
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Official URL: http://dx.doi.org/10.1680/bbn.14.00006
Abstract
This study investigated the correlation of age in male and female specimens with physico-mechanical properties of trabecular bone including compressive strength, bone volume fraction, structural model index, trabecular thickness factor, level of inter-connectivity and pore morphology. An artificial neural network was designed to analyse 35 available samples in order to account for complex inter-dependencies of the key parameters in multi-dimensional space. Trained by using Levenberg-Marquardt back propagation algorithm, the network achieved regression factor of 0ยท96 by optimisation and showed that age correlates strongly with the physical properties of the bone affected by severe osteoarthritis. In addition, the compressive strength was found to be the most important factor for predicting the bone aging. Within the limitations of the input data set, the model developed provides a reliable predictive tool to tissue engineering applications.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QM Human anatomy | ||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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Library of Congress Subject Headings (LCSH): | Skeleton -- Compression testing | ||||||||
Journal or Publication Title: | Bioinspired, Biomimetic and Nanobiomaterials | ||||||||
Publisher: | I C E Publishing | ||||||||
ISSN: | 2045-9858 | ||||||||
Official Date: | 23 March 2014 | ||||||||
Dates: |
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Number of Pages: | 11 | ||||||||
Page Range: | pp. 1-11 | ||||||||
DOI: | 10.1680/bbn.14.00006 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Funder: | Engineering and Physical Sciences Research Council (EPSRC) | ||||||||
Grant number: | EP/K02504X/1 (EPSRC) | ||||||||
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