Neural Network Prediction of the Trabecular Bone Mechanical Competence Parameter

Detalhes bibliográficos
Autor(a) principal: Filletti, E. R. [UNESP]
Data de Publicação: 2014
Outros Autores: Roque, W. L., Braidot, A., Hadad, A.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-319-13117-7_59
http://hdl.handle.net/11449/183943
Resumo: The 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.
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spelling Neural Network Prediction of the Trabecular Bone Mechanical Competence ParameterTrabecular bonemechanical competenceartificial neural networklearningosteoporosisThe 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.Univ Estadual Paulista, Inst Quim, BR-14800060 Araraquara, SP, BrazilUniv Fed Paraiba, Dept Comp Cient, BR-58051900 Joao Pessoa, Paraiba, BrazilUniv Estadual Paulista, Inst Quim, BR-14800060 Araraquara, SP, BrazilSpringerUniversidade Estadual Paulista (Unesp)Univ Fed ParaibaFilletti, E. R. [UNESP]Roque, W. L.Braidot, A.Hadad, A.2019-10-03T18:18:27Z2019-10-03T18:18:27Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject226-229http://dx.doi.org/10.1007/978-3-319-13117-7_59Vi Latin American Congress On Biomedical Engineering (claib 2014). Cham: Springer Int Publishing Ag, v. 49, p. 226-229, 2014.1680-0737http://hdl.handle.net/11449/18394310.1007/978-3-319-13117-7_59WOS:000363767200058Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengVi Latin American Congress On Biomedical Engineering (claib 2014)info:eu-repo/semantics/openAccess2021-10-23T20:17:32Zoai:repositorio.unesp.br:11449/183943Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:03:23.182317Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Neural Network Prediction of the Trabecular Bone Mechanical Competence Parameter
title Neural Network Prediction of the Trabecular Bone Mechanical Competence Parameter
spellingShingle Neural Network Prediction of the Trabecular Bone Mechanical Competence Parameter
Filletti, E. R. [UNESP]
Trabecular bone
mechanical competence
artificial neural network
learning
osteoporosis
title_short Neural Network Prediction of the Trabecular Bone Mechanical Competence Parameter
title_full Neural Network Prediction of the Trabecular Bone Mechanical Competence Parameter
title_fullStr Neural Network Prediction of the Trabecular Bone Mechanical Competence Parameter
title_full_unstemmed Neural Network Prediction of the Trabecular Bone Mechanical Competence Parameter
title_sort Neural Network Prediction of the Trabecular Bone Mechanical Competence Parameter
author Filletti, E. R. [UNESP]
author_facet Filletti, E. R. [UNESP]
Roque, W. L.
Braidot, A.
Hadad, A.
author_role author
author2 Roque, W. L.
Braidot, A.
Hadad, A.
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Univ Fed Paraiba
dc.contributor.author.fl_str_mv Filletti, E. R. [UNESP]
Roque, W. L.
Braidot, A.
Hadad, A.
dc.subject.por.fl_str_mv Trabecular bone
mechanical competence
artificial neural network
learning
osteoporosis
topic Trabecular bone
mechanical competence
artificial neural network
learning
osteoporosis
description The 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.
publishDate 2014
dc.date.none.fl_str_mv 2014-01-01
2019-10-03T18:18:27Z
2019-10-03T18:18:27Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1007/978-3-319-13117-7_59
Vi Latin American Congress On Biomedical Engineering (claib 2014). Cham: Springer Int Publishing Ag, v. 49, p. 226-229, 2014.
1680-0737
http://hdl.handle.net/11449/183943
10.1007/978-3-319-13117-7_59
WOS:000363767200058
url http://dx.doi.org/10.1007/978-3-319-13117-7_59
http://hdl.handle.net/11449/183943
identifier_str_mv Vi Latin American Congress On Biomedical Engineering (claib 2014). Cham: Springer Int Publishing Ag, v. 49, p. 226-229, 2014.
1680-0737
10.1007/978-3-319-13117-7_59
WOS:000363767200058
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Vi Latin American Congress On Biomedical Engineering (claib 2014)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 226-229
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808129485306855424

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