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dc.contributor.authorFilletti, E. R. [UNESP]
dc.contributor.authorRoque, W. L.
dc.contributor.authorBraidot, A.
dc.contributor.authorHadad, A.
dc.date.accessioned2019-10-03T18:18:27Z
dc.date.available2019-10-03T18:18:27Z
dc.date.issued2014-01-01
dc.identifierhttp://dx.doi.org/10.1007/978-3-319-13117-7_59
dc.identifier.citationVi Latin American Congress On Biomedical Engineering (claib 2014). Cham: Springer Int Publishing Ag, v. 49, p. 226-229, 2014.
dc.identifier.issn1680-0737
dc.identifier.urihttp://hdl.handle.net/11449/183943
dc.description.abstractThe mechanical competence parameter (MCP) has been defined to grade the trabecular bone fragility based on the principal component analysis (PCA) evaluated in terms of volume fraction, connectivity, tortuosity and Young modulus of elasticity. Using a set of 83 in vivo distal radius magnetic resonance image samples, an artificial neural network (ANN) has been trained to predict the MCP. After the learning phase, the ANN was able to predict the MCP for 20 new samples with very high accuracy. It is shown that there is a strong correlation (r = 0.99) between the MCP estimated by PCA and ANN techniques. In addition, the Bland-Altman plot provides evidence that the PCA and ANN are reasonably comparable techniques to estimate the MCP.en
dc.format.extent226-229
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofVi Latin American Congress On Biomedical Engineering (claib 2014)
dc.sourceWeb of Science
dc.subjectTrabecular bone
dc.subjectmechanical competence
dc.subjectartificial neural network
dc.subjectlearning
dc.subjectosteoporosis
dc.titleNeural Network Prediction of the Trabecular Bone Mechanical Competence Parameteren
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
dcterms.rightsHolderSpringer
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniv Fed Paraiba
dc.description.affiliationUniv Estadual Paulista, Inst Quim, BR-14800060 Araraquara, SP, Brazil
dc.description.affiliationUniv Fed Paraiba, Dept Comp Cient, BR-58051900 Joao Pessoa, Paraiba, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Inst Quim, BR-14800060 Araraquara, SP, Brazil
dc.identifier.doi10.1007/978-3-319-13117-7_59
dc.identifier.wosWOS:000363767200058
dc.rights.accessRightsAcesso aberto
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