Sex Estimation From the Paranasal Sinus Volumes Using Semiautomatic Segmentation, Discriminant Analyses, and Machine Learning Algorithms

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dc.authoridTASTEKIN, Burak/0000-0002-8617-1059
dc.authoridAsirdizer, Mahmut/0000-0001-7596-5892
dc.authoridEtli, Yasin/0000-0002-7369-6083
dc.contributor.authorHekimoglu, Yavuz
dc.contributor.authorSasani, Hadi
dc.contributor.authorEtli, Yasin
dc.contributor.authorKeskin, Siddik
dc.contributor.authorTastekin, Burak
dc.contributor.authorAsirdizer, Mahmut
dc.date.accessioned2024-10-29T17:58:41Z
dc.date.available2024-10-29T17:58:41Z
dc.date.issued2023
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractThe aims of this study were to determine whether paranasal sinus volumetric measurements differ according to sex, age group, and right-left side and to determine the rate of sexual dimorphism using discriminant function analysis and machine learning algorithms. The study included paranasal computed tomography images of 100 live individuals of known sex and age. The paranasal sinuses were marked using semiautomatic segmentation and their volumes and densities were measured. Sex determination using discriminant analyses and machine learning algorithms was performed. Males had higher mean volumes of all paranasal sinuses than females (P < 0.05); however, there were no statistically significant differences between age groups or sides (P > 0.05). The paranasal sinus volumes of females were more dysmorphic during sex determination. The frontal sinus volume had the highest accuracy, whereas the sphenoid sinus volume was the least dysmorphic. In this study, although there was moderate sexual dimorphism in paranasal sinus volumes, the use of machine learning methods increased the accuracy of sex estimation. We believe that sex estimation rates will be significantly higher in future studies that combine linear measurements, volumetric measurements, and machine-learning algorithms.
dc.identifier.doi10.1097/PAF.0000000000000842
dc.identifier.endpage320
dc.identifier.issn0195-7910
dc.identifier.issn1533-404X
dc.identifier.issue4en_US
dc.identifier.pmid37235867
dc.identifier.scopus2-s2.0-85178496068
dc.identifier.scopusqualityQ3
dc.identifier.startpage311
dc.identifier.urihttps://doi.org/10.1097/PAF.0000000000000842
dc.identifier.urihttps://hdl.handle.net/20.500.11776/14449
dc.identifier.volume44
dc.identifier.wosWOS:001208558600006
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherLippincott Williams & Wilkins
dc.relation.ispartofAmerican Journal of Forensic Medicine and Pathology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectparanasal sinus volume
dc.subjectdiscriminant function analysis
dc.subjectidentification
dc.subjectanthropology
dc.subjectmachine learning
dc.subjectartificial intelligence
dc.titleSex Estimation From the Paranasal Sinus Volumes Using Semiautomatic Segmentation, Discriminant Analyses, and Machine Learning Algorithms
dc.typeArticle

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