Gender prediction with the parameters obtained from pelvis computed tomography images and machine learning algorithms

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dc.authoridOner, Serkan/0000-0002-7802-880X
dc.authoridSECGIN, YUSUF/0000-0002-0118-6711
dc.authorwosidOner, Serkan/T-2518-2019
dc.contributor.authorSecgin, Yusuf
dc.contributor.authorOner, Zulal
dc.contributor.authorTuran, Muhammed Kamil
dc.contributor.authorOner, Serkan
dc.date.accessioned2023-03-22T19:47:28Z
dc.date.available2023-03-22T19:47:28Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description.abstractIntroduction: In the skeletal system, the most dimorphic bones employed for postmortem gender prediction include the bones in the pelvic skeleton. Bone measurements are usually conducted with cadaver bones. Computed tomography (CT) is an increasingly popular method due to its ease of use, reconstruction opportunities, and lower impact of age bias and provides a modern data source. Even when parameters obtained with different or same bones are missing, machine learning (ML) algorithms allow the use of statistical methods to predict gender. This study was carried out in order to obtain high accuracy in estimating gender with the pelvis skeleton by integrating ML algorithms, which are used extensively in the field of engineering, in the field of health. Material and Methods: In the present study, pelvic CT images of 300 healthy individuals (150 females, 150 males) between the ages of 25 and 50 (the mean female age = 40, the mean male age = 37) were transformed into orthogonal images, and landmarks were placed on promontory, iliac crest, sacroiliac joint, anterior superior iliac spine, anterior inferior iliac spine, terminal line, obturator foramen, greater trochanter, lesser trochanter, femoral head, femoral neck, body of femur, ischial tuberosity, acetabulum, and pubic symphysis, and coordinates of these regions were obtained. Four groups were formed based on various angle and length combinations obtained from these coordinates. These four groups were analyzed with ML algorithms such as Logistic Regression, Linear Discriminant Analysis (LDA), Random Forest, Extra Trees Classifier, and ADA Boost Classifier. Results: In the analysis, it was determined that the highest accuracy was 0.96 (sensitivity 0.95, specificity 0.97, Matthew's Correlation Coefficient 0.93) with LDA. Discussion and Conclusion: The use of length and angle measurements obtained from the pelvis showed that the LDA model was effective in estimating gender.en_US
dc.identifier.doi10.4103/jasi.jasi_280_20
dc.identifier.endpage209en_US
dc.identifier.issn0003-2778
dc.identifier.issn2352-3050
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85139395862en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage204en_US
dc.identifier.urihttps://doi.org/10.4103/jasi.jasi_280_20
dc.identifier.urihttps://hdl.handle.net/20.500.14034/722
dc.identifier.volume71en_US
dc.identifier.wosWOS:000864606500007en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWolters Kluwer Medknow Publicationsen_US
dc.relation.journalJournal Of The Anatomical Society Of Indiaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputed tomographyen_US
dc.subjectgender predictionen_US
dc.subjectmachine learning algorithmsen_US
dc.subjectpelvisen_US
dc.subjectSex Estimationen_US
dc.titleGender prediction with the parameters obtained from pelvis computed tomography images and machine learning algorithmsen_US
dc.typeArticleen_US

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