Sex estimation with the total area of the proximal femur: A densitometric approach

Detalhes bibliográficos
Autor(a) principal: Curate, Francisco
Data de Publicação: 2017
Outros Autores: Albuquerque, Anabela, Ferreira, Izilda, Cunha, Eugénia
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/104441
https://doi.org/10.1016/j.forsciint.2017.02.035
Resumo: The estimation of sex is a central step to establish the biological profile of an anonymous skeletal individual. Imaging techniques, including bone densitometry, have been used to evaluate sex in remains incompletely skeletonized. In this paper, we present a technique for sex estimation using the total area (TA) of the proximal femur, a two-dimensional areal measurement determined through densitometry. TA was acquired from a training sample (112 females; 112 males) from the Coimbra Identified Skeletal Collection (University of Coimbra, Portugal). Logistic regression (LR), linear discriminant analysis (LDA), reduce error pruning trees (REPTree), and classification and regression trees (CART) were employed in order to obtain models that could predict sex in unidentified skeletal remains. Under cross-validation, the proposed models correctly estimated sex in 90.2–92.0% of cases (bias ranging from 1.8% to 4.5%). The models were evaluated in an independent test sample (30 females; 30 males) from the 21st Century Identified Skeletal Collection (University of Coimbra, Portugal), with a sex allocation accuracy ranging from 90.0% to 91.7% (bias from 3.3% to 10.0%). Overall, data mining classifiers, especially the REPTree, performed better than the traditional classifiers (LR and LDA), maximizing overall accuracy and minimizing bias. This study emphasizes the significant value of bone densitometry to estimate sex in cadaveric remains in diverse states of preservation and completeness, even human remains with soft tissues.
id RCAP_32aabbc8333f4507aa5dd7f215306e95
oai_identifier_str oai:estudogeral.uc.pt:10316/104441
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Sex estimation with the total area of the proximal femur: A densitometric approachbiological profilesex estimationfemurosteodensitometryforensic anthropologybioarchaeologyThe estimation of sex is a central step to establish the biological profile of an anonymous skeletal individual. Imaging techniques, including bone densitometry, have been used to evaluate sex in remains incompletely skeletonized. In this paper, we present a technique for sex estimation using the total area (TA) of the proximal femur, a two-dimensional areal measurement determined through densitometry. TA was acquired from a training sample (112 females; 112 males) from the Coimbra Identified Skeletal Collection (University of Coimbra, Portugal). Logistic regression (LR), linear discriminant analysis (LDA), reduce error pruning trees (REPTree), and classification and regression trees (CART) were employed in order to obtain models that could predict sex in unidentified skeletal remains. Under cross-validation, the proposed models correctly estimated sex in 90.2–92.0% of cases (bias ranging from 1.8% to 4.5%). The models were evaluated in an independent test sample (30 females; 30 males) from the 21st Century Identified Skeletal Collection (University of Coimbra, Portugal), with a sex allocation accuracy ranging from 90.0% to 91.7% (bias from 3.3% to 10.0%). Overall, data mining classifiers, especially the REPTree, performed better than the traditional classifiers (LR and LDA), maximizing overall accuracy and minimizing bias. This study emphasizes the significant value of bone densitometry to estimate sex in cadaveric remains in diverse states of preservation and completeness, even human remains with soft tissues.Elsevier2017-06info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/104441http://hdl.handle.net/10316/104441https://doi.org/10.1016/j.forsciint.2017.02.035enghttps://www.sciencedirect.com/science/article/abs/pii/S0379073817300890?via%3DihubCurate, FranciscoAlbuquerque, AnabelaFerreira, IzildaCunha, Eugéniainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-01-12T12:34:26Zoai:estudogeral.uc.pt:10316/104441Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:21:09.480178Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Sex estimation with the total area of the proximal femur: A densitometric approach
title Sex estimation with the total area of the proximal femur: A densitometric approach
spellingShingle Sex estimation with the total area of the proximal femur: A densitometric approach
Curate, Francisco
biological profile
sex estimation
femur
osteodensitometry
forensic anthropology
bioarchaeology
title_short Sex estimation with the total area of the proximal femur: A densitometric approach
title_full Sex estimation with the total area of the proximal femur: A densitometric approach
title_fullStr Sex estimation with the total area of the proximal femur: A densitometric approach
title_full_unstemmed Sex estimation with the total area of the proximal femur: A densitometric approach
title_sort Sex estimation with the total area of the proximal femur: A densitometric approach
author Curate, Francisco
author_facet Curate, Francisco
Albuquerque, Anabela
Ferreira, Izilda
Cunha, Eugénia
author_role author
author2 Albuquerque, Anabela
Ferreira, Izilda
Cunha, Eugénia
author2_role author
author
author
dc.contributor.author.fl_str_mv Curate, Francisco
Albuquerque, Anabela
Ferreira, Izilda
Cunha, Eugénia
dc.subject.por.fl_str_mv biological profile
sex estimation
femur
osteodensitometry
forensic anthropology
bioarchaeology
topic biological profile
sex estimation
femur
osteodensitometry
forensic anthropology
bioarchaeology
description The estimation of sex is a central step to establish the biological profile of an anonymous skeletal individual. Imaging techniques, including bone densitometry, have been used to evaluate sex in remains incompletely skeletonized. In this paper, we present a technique for sex estimation using the total area (TA) of the proximal femur, a two-dimensional areal measurement determined through densitometry. TA was acquired from a training sample (112 females; 112 males) from the Coimbra Identified Skeletal Collection (University of Coimbra, Portugal). Logistic regression (LR), linear discriminant analysis (LDA), reduce error pruning trees (REPTree), and classification and regression trees (CART) were employed in order to obtain models that could predict sex in unidentified skeletal remains. Under cross-validation, the proposed models correctly estimated sex in 90.2–92.0% of cases (bias ranging from 1.8% to 4.5%). The models were evaluated in an independent test sample (30 females; 30 males) from the 21st Century Identified Skeletal Collection (University of Coimbra, Portugal), with a sex allocation accuracy ranging from 90.0% to 91.7% (bias from 3.3% to 10.0%). Overall, data mining classifiers, especially the REPTree, performed better than the traditional classifiers (LR and LDA), maximizing overall accuracy and minimizing bias. This study emphasizes the significant value of bone densitometry to estimate sex in cadaveric remains in diverse states of preservation and completeness, even human remains with soft tissues.
publishDate 2017
dc.date.none.fl_str_mv 2017-06
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/104441
http://hdl.handle.net/10316/104441
https://doi.org/10.1016/j.forsciint.2017.02.035
url http://hdl.handle.net/10316/104441
https://doi.org/10.1016/j.forsciint.2017.02.035
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.sciencedirect.com/science/article/abs/pii/S0379073817300890?via%3Dihub
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799134102791651328