Sex estimation using sternum part lenghts by means of artificial neural networks

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dc.authoridSECGIN, YUSUF/0000-0002-0118-6711
dc.authoridTuran, Muhammed Kamil/0000-0002-1086-9514
dc.authoridOner, Serkan/0000-0002-7802-880X
dc.authoridSahin, Bunyamin/0000-0001-8538-8443
dc.authoridONER, ZULAL/0000-0003-0459-1015
dc.contributor.authorOner, Zulal
dc.contributor.authorTuran, Muhammed Kamil
dc.contributor.authorOner, Serkan
dc.contributor.authorSecgin, Yusuf
dc.contributor.authorSahin, Bunyamin
dc.date.accessioned2024-09-29T15:57:11Z
dc.date.available2024-09-29T15:57:11Z
dc.date.issued2019
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIn addition to the pelvis, cranium and phalanges, the sternum is also used for postmortem sex identification. Bone measurements may be obtained on cadaveric bones. Alternatively, computerized tomography may be used to obtain measurements close to the original ones. Moreover, usage of artificial neural networks (ANNs) in the field of medicine has started to provide new horizons. In this study, we aimed to identify sex by an ANN using lengths of manubrium sterni (MSL), corpus sterni (CSL) and processus xiphoideus (XPL) and sternal angle (SA) from computerized tomography (CT) images brought to an orthogonal plane. This study used the thin-slice thoracic CT images of 422 cases (213 female, 209 male) with an age range of 27-60 years brought to the orthogonal plane. Measurements of MSL, CSL, XPL and SA were analyzed with a multilayer artificial neural network that used stochastic gradient descent (SGD) for optimization and two hidden layers. MSL, CSL and XPL were longer, and SA was wider in men (MSL p = 0.000, CSL p = 0.000, XPL p = 0.000, SA p = 0.02). In the case of the two hidden layers of the network with 20 and 14 neurons in the hidden layers, respectively, learning rate of 0.1 and momentum coefficient of 0.9, the accuracy (Acc) of sex prediction was 0.906. In order to define a more realistic performance of the network, bootstrap was run with the confidence interval of 94%. A sensitivity (Sen) value of 0.91 and a specificity (Spe) value of 0.90 were calculated. The success rates that were achieved in sex identification with measurements on the skeleton using ANN were observed to be higher than those achieved by linear models. Also, sometimes all parts of the bones may not be found or might be deformed. In this case, the number of parameters used for the estimation will be incomplete. The ANN has the strong advantage to be able to estimate despite the missing parameter. (C) 2019 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.forsciint.2019.05.011
dc.identifier.endpage11en_US
dc.identifier.issn0379-0738
dc.identifier.issn1872-6283
dc.identifier.pmid31128410en_US
dc.identifier.scopus2-s2.0-85065864909en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage6en_US
dc.identifier.urihttps://doi.org/10.1016/j.forsciint.2019.05.011
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4654
dc.identifier.volume301en_US
dc.identifier.wosWOS:000473261300012en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofForensic Science Internationalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSternumen_US
dc.subjectComputerized tomographyen_US
dc.subjectArtificial neural networken_US
dc.subjectMultilayer perceptron classifieren_US
dc.subjectStochastic gradient descenten_US
dc.subjectSex identificationen_US
dc.titleSex estimation using sternum part lenghts by means of artificial neural networksen_US
dc.typeArticleen_US

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