Age Estimation Using Machine Learning Algorithms with Parameters Obtained from X-ray Images of the Calcaneus

dc.authoridOner, Serkan/0000-0002-7802-880X
dc.authoridSECGIN, YUSUF/0000-0002-0118-6711
dc.contributor.authorCiftci, R.
dc.contributor.authorSecgin, Y.
dc.contributor.authorOner, Z.
dc.contributor.authorToy, S.
dc.contributor.authorOner, S.
dc.date.accessioned2024-09-29T16:08:27Z
dc.date.available2024-09-29T16:08:27Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractBackground:Determination of bone age is a critical issue for forensics, surgery, and basic sciences.Aim:This study aims to estimate age with high accuracy and precision using Machine Learning (ML) algorithms with parameters obtained from calcaneus x-ray images of healthy individuals.Method:The study was carried out by retrospectively examining the foot X-ray images of 341 people aged 18-65 years. Maximum width of the calcaneus (MW), body width (BW), maximum length (MAXL), minimum length (MINL), facies articularis cuboidea height (FACH), maximum height (MAXH), and tuber calcanei width (TKW) parameters were measured from the images. The measurements were then grouped as 20-45 years of age, 46-64 years of age, 65 and older, and age estimation was made by using these at the input of ML models.Results:As a result of the ML input of the measurements obtained, a 0.85 Accuracy (Acc) rate was obtained with the Extra Tree Classifier algorithm. The accuracy rate of other algorithms was found to vary between 0.78 and 0.82. The contribution of parameters to the overall result was evaluated by using the shapley additive explanations (SHAP) analyzer of Random Forest algorithm and the MAXH parameter was found to have the highest contribution in age estimation.Conclusions:As a result of our study, calcaneus bone was found to have high accuracy and precision in age estimations.en_US
dc.identifier.doi10.4103/njcp.njcp_602_23
dc.identifier.endpage214en_US
dc.identifier.issn1119-3077
dc.identifier.issn2229-7731
dc.identifier.issue2en_US
dc.identifier.pmid38409149en_US
dc.identifier.scopus2-s2.0-85186388522en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage209en_US
dc.identifier.urihttps://doi.org/10.4103/njcp.njcp_602_23
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7558
dc.identifier.volume27en_US
dc.identifier.wosWOS:001177325400003en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherWolters Kluwer Medknow Publicationsen_US
dc.relation.ispartofNigerian Journal of Clinical Practiceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAge estimationen_US
dc.subjectcalcaneusen_US
dc.subjectmachine learning algorithmsen_US
dc.subjectx-rayen_US
dc.titleAge Estimation Using Machine Learning Algorithms with Parameters Obtained from X-ray Images of the Calcaneusen_US
dc.typeArticleen_US

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