Estimating the mechanical competence parameter of the trabecular bone: a neural network approach

Detalhes bibliográficos
Autor(a) principal: Filletti,Érica Regina
Data de Publicação: 2016
Outros Autores: Roque,Waldir Leite
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Research on Biomedical Engineering (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402016000200137
Resumo: Abstract Introduction The mechanical competence parameter (MCP) of the trabecular bone is a parameter that merges the volume fraction, connectivity, tortuosity and Young modulus of elasticity, to provide a single measure of the trabecular bone structural quality. Methods As the MCP is estimated for 3D images and the Young modulus simulations are quite consuming, in this paper, an alternative approach to estimate the MCP based on artificial neural network (ANN) is discussed considering as the training set a group of 23 in vitro vertebrae and 12 distal radius samples obtained by microcomputed tomography (μCT), and 83 in vivo distal radius magnetic resonance image samples (MRI). Results It is shown that the ANN was able to predict with very high accuracy the MCP for 29 new samples, being 6 vertebrae and 3 distal radius bones by μCT and 20 distal radius bone by MRI. Conclusion There is a strong correlation (R2 = 0.97) between both techniques and, despite the small number of testing samples, the Bland-Altman analysis shows that ANN is within the limits of agreement to estimate the MCP.
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spelling Estimating the mechanical competence parameter of the trabecular bone: a neural network approachOsteoporosisTrabecular boneMechanical competenceArtificial neural networkMachine learning Abstract Introduction The mechanical competence parameter (MCP) of the trabecular bone is a parameter that merges the volume fraction, connectivity, tortuosity and Young modulus of elasticity, to provide a single measure of the trabecular bone structural quality. Methods As the MCP is estimated for 3D images and the Young modulus simulations are quite consuming, in this paper, an alternative approach to estimate the MCP based on artificial neural network (ANN) is discussed considering as the training set a group of 23 in vitro vertebrae and 12 distal radius samples obtained by microcomputed tomography (μCT), and 83 in vivo distal radius magnetic resonance image samples (MRI). Results It is shown that the ANN was able to predict with very high accuracy the MCP for 29 new samples, being 6 vertebrae and 3 distal radius bones by μCT and 20 distal radius bone by MRI. Conclusion There is a strong correlation (R2 = 0.97) between both techniques and, despite the small number of testing samples, the Bland-Altman analysis shows that ANN is within the limits of agreement to estimate the MCP.Sociedade Brasileira de Engenharia Biomédica2016-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402016000200137Research on Biomedical Engineering v.32 n.2 2016reponame:Research on Biomedical Engineering (Online)instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)instacron:SBEB10.1590/2446-4740.05615info:eu-repo/semantics/openAccessFilletti,Érica ReginaRoque,Waldir Leiteeng2016-07-21T00:00:00Zoai:scielo:S2446-47402016000200137Revistahttp://www.rbejournal.org/https://old.scielo.br/oai/scielo-oai.php||rbe@rbejournal.org2446-47402446-4732opendoar:2016-07-21T00:00Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB)false
dc.title.none.fl_str_mv Estimating the mechanical competence parameter of the trabecular bone: a neural network approach
title Estimating the mechanical competence parameter of the trabecular bone: a neural network approach
spellingShingle Estimating the mechanical competence parameter of the trabecular bone: a neural network approach
Filletti,Érica Regina
Osteoporosis
Trabecular bone
Mechanical competence
Artificial neural network
Machine learning
title_short Estimating the mechanical competence parameter of the trabecular bone: a neural network approach
title_full Estimating the mechanical competence parameter of the trabecular bone: a neural network approach
title_fullStr Estimating the mechanical competence parameter of the trabecular bone: a neural network approach
title_full_unstemmed Estimating the mechanical competence parameter of the trabecular bone: a neural network approach
title_sort Estimating the mechanical competence parameter of the trabecular bone: a neural network approach
author Filletti,Érica Regina
author_facet Filletti,Érica Regina
Roque,Waldir Leite
author_role author
author2 Roque,Waldir Leite
author2_role author
dc.contributor.author.fl_str_mv Filletti,Érica Regina
Roque,Waldir Leite
dc.subject.por.fl_str_mv Osteoporosis
Trabecular bone
Mechanical competence
Artificial neural network
Machine learning
topic Osteoporosis
Trabecular bone
Mechanical competence
Artificial neural network
Machine learning
description Abstract Introduction The mechanical competence parameter (MCP) of the trabecular bone is a parameter that merges the volume fraction, connectivity, tortuosity and Young modulus of elasticity, to provide a single measure of the trabecular bone structural quality. Methods As the MCP is estimated for 3D images and the Young modulus simulations are quite consuming, in this paper, an alternative approach to estimate the MCP based on artificial neural network (ANN) is discussed considering as the training set a group of 23 in vitro vertebrae and 12 distal radius samples obtained by microcomputed tomography (μCT), and 83 in vivo distal radius magnetic resonance image samples (MRI). Results It is shown that the ANN was able to predict with very high accuracy the MCP for 29 new samples, being 6 vertebrae and 3 distal radius bones by μCT and 20 distal radius bone by MRI. Conclusion There is a strong correlation (R2 = 0.97) between both techniques and, despite the small number of testing samples, the Bland-Altman analysis shows that ANN is within the limits of agreement to estimate the MCP.
publishDate 2016
dc.date.none.fl_str_mv 2016-06-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402016000200137
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402016000200137
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/2446-4740.05615
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Engenharia Biomédica
publisher.none.fl_str_mv Sociedade Brasileira de Engenharia Biomédica
dc.source.none.fl_str_mv Research on Biomedical Engineering v.32 n.2 2016
reponame:Research on Biomedical Engineering (Online)
instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)
instacron:SBEB
instname_str Sociedade Brasileira de Engenharia Biomédica (SBEB)
instacron_str SBEB
institution SBEB
reponame_str Research on Biomedical Engineering (Online)
collection Research on Biomedical Engineering (Online)
repository.name.fl_str_mv Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB)
repository.mail.fl_str_mv ||rbe@rbejournal.org
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