Evidence map›Paper›PMID 41848882›Full record

ArticleSurgical and radiologic anatomy : SRA2026

Sex estimation from sacral anatomy in Turkish adults: a machine learning-based analysis.

Busra Gul Ayturk, Ali Keles, Usame Omer Osmanoglu

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Article in Surgical and radiologic anatomy : SRA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Busra Gul AyturkThe Institute of Health Sciences, Department of Anatomy, Karamanoglu Mehmetbey University, 70200, Karaman, Turkey. ayturkbusragul@gmail.com.ORCID http://orcid.org/0009-0007-8928-3586
Ali KelesFaculty of Medicine, Department of Anatomy, Karamanoglu Mehmetbey University, Karaman, Turkey.ORCID http://orcid.org/0000-0002-0807-937X
Usame Omer OsmanogluFaculty of Medicine, Department of Biostatistics, Karamanoglu Mehmetbey University, Karaman, Turkey.ORCID http://orcid.org/0000-0002-1198-2447

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe estimation of sex is the first and foremost step in developing a reliable biological identity during the examination of skeletal remains. The purpose of the study is to assess the usability of the sacrum for sex determination in a Turkish population through machine learning.

methodsWe performed a retrospective examination of the CT images of 540 individuals. DICOM images, including the sacrum, were obtained from patients aged 18–79 years. The following parameters were measured: Anterior Sacral Length (ASL), Posterior Sacral Length (PSL), Anterior Sacrococcygeal Length (ASCL), Posterior Sacrococcygeal Length (PSCL), Maximum Anteroposterior Diameter (MAPD), and Maximum Transverse Diameter (MTD). Eight different machine learning algorithms were applied for sex estimation. To enhance reliability, fivefold cross-validation was used.

resultsSacral morphometric parameters were statistically significantly higher in males than in females (p < 0.001). Among the algorithms tested, logistic regression achieved the highest classification accuracy for sex at 85%. Based on these results, logistic regression provided the best performance for sex estimation from sacral measurements. Further, the maximum anteroposterior diameter at the sacral base was the most discriminative parameter.

conclusionBased on our analysis shows that the sacrum is a reliable anatomical structure for predicting sex in cases of high sexual dimorphism with an accuracy rate of 85%. The sacrum can serve as a powerful reference for sex estimation in forensic cases and anthropological applications. Furthermore, the resulting morphometric records can be stored as a useful resource for preoperative planning and anatomical evaluation for clinics and regional surgeons.

Indexed as

Machine LearningSacrumSex Determination by SkeletonAdolescentAdultAgedAlgorithmsFemaleHumansMaleMiddle AgedReproducibility of ResultsRetrospective StudiesTomography, X-Ray ComputedTurkeyYoung AdultDimorphismMachine learningMorphometrySacrumSex estimation

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.