Hip osteoarthritis: A novel network analysis of subchondral trabecular bone structures

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dc.contributor.authorDorraki, M.
dc.contributor.authorMuratovic, D.
dc.contributor.authorFouladzadeh, A.
dc.contributor.authorVerjans, J.W.
dc.contributor.authorAllison, A.
dc.contributor.authorFindlay, D.M.
dc.contributor.authorAbbott, D.
dc.contributor.editorYooseph, S.
dc.date.issued2022
dc.description.abstractHip osteoarthritis (HOA) is a degenerative joint disease that leads to the progressive destruction of subchondral bone and cartilage at the hip joint. Development of effective treatments for HOA remains an open problem, primarily due to the lack of knowledge of its pathogenesis and a typically late-stage diagnosis.We describe a novel network analysis methodology for microcomputed tomography (micro-CT) images of human trabecular bone.We explored differences between the trabecular bone microstructure of femoral heads with and without HOA. Large-scale automated extraction of the network formed by trabecular bone revealed significant network properties not previously reported for bone. Profound differences were discovered, particularly in the proximal third of the femoral head, where HOA networks demonstrated elevated numbers of edges, vertices, and graph components. When further differentiating healthy joint and HOA networks, the latter showed fewer small-world network properties, due to decreased clustering coefficient and increased characteristic path length. Furthermore,we found that HOA networks had reduced length of edges, indicating the formation of compressed trabecular structures. In order to assess our network approach,we developed a deep learningmodel for classifying HOA and control cases, and we fed it with two separate inputs: (i) micro-CT images of the trabecular bone, and (ii) the network extracted from them. The model with plain micro-CT images achieves 74.6% overall accuracy while the trained model with extracted networks attains 96.5% accuracy. We anticipate our findings to be a starting point for a novel description of bone microstructure in HOA, by considering the phenomenon from a graph theory viewpoint.
dc.description.statementofresponsibilityMohsen Dorraki, Dzenita Muratovic, Anahita Fouladzadeh, Johan W. Verjans, Andrew Allison, David M. Findlay and Derek Abbott
dc.identifier.citationPNAS Nexus, 2022; 1(5):pgac258-1-pgac258-11
dc.identifier.doi10.1093/pnasnexus/pgac258
dc.identifier.issn2752-6542
dc.identifier.issn2752-6542
dc.identifier.orcidDorraki, M. [0000-0002-6675-3393]
dc.identifier.orcidMuratovic, D. [0000-0002-3009-7881]
dc.identifier.orcidVerjans, J.W. [0000-0002-8336-6774]
dc.identifier.orcidAllison, A. [0000-0003-3865-511X]
dc.identifier.orcidAbbott, D. [0000-0002-0945-2674]
dc.identifier.urihttps://hdl.handle.net/2440/138558
dc.language.isoen
dc.publisherOxford University Press (OUP)
dc.relation.grantNHMRC
dc.rights© The Author(s) 2022. Published by Oxford University Press on behalf of National Academy of Sciences. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.source.urihttps://doi.org/10.1093/pnasnexus/pgac258
dc.subjectosteoarthritis; graph theory; networks; machine learning; convolutional neural networks
dc.titleHip osteoarthritis: A novel network analysis of subchondral trabecular bone structures
dc.typeJournal article
pubs.publication-statusPublished

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