Kraniyofasiyal boyutlar kullanarak cinsiyet tahmini: Kaduna Eyaleti, Nijerya'da BT tarama görüntüleri çalışması

Author:

JAAFAR Aliyu1ORCID,MURDAKAİ Tanko2ORCID,TERSOO Moses AsonguORCID,MUHAMMAD AbdulrazakORCID,BAUCHİ Zainab M.ORCID,FARRAU UsmanORCID,ALİYU Ibrahim SamboORCID,ADAMU Lawan H.ORCID,IBRAHİM Muhammad ZariaORCID,NADABO ABDULLAHİ YusufORCID,ZAHARADDEEN MUHAMMAD YUSUF Zaharaddeen MuhammadORCID,JAAFAR AmiruORCID

Affiliation:

1. Ahmadu Bello University Zaria

2. Ahmadu Bello University

Abstract

Purpose: The aim of this study is to evaluate the potential of craniofacial dimensions in estimating sex in a sample population in Kaduna State, Nigeria. Materials and Methods: This is a retrospective study of normal CT scan images of 399 Crania (comprising 236 males and 163 females) of age range 18–95 years that came for CT scans for the diagnostic purpose at the National Ear Care Centre, Kaduna between the years of 2017–2019. The images were randomly taken at the archives of the Radiology Department of the institute on an axial plane. The five craniofacial dimensions were measured directly from the computer screen using Vitrea CT Software. Results: Maximum cranial width (13.49±0.57 cm), maximum cranial length (18.11±0.74 cm), and bizygomatic length (12.64±0.58 cm) of males were significantly greater than in females (13.35±0.49 cm), (17.82±0.66 cm) and (12.22±0.59 cm) respectively. The bizygomatic length on the receiver operating characteristic curve (Area under the curve = 0.711), logistic regression (odd ratio = 1.254), and discriminant function analysis (percentage accuracy after cross validation = 67.4 %.) was the best single variable for estimating sex. Bizygomatic and maximum cranial length were selected as the significant estimators of sex by multivariate logistic regression with Adjusted Odd Ratios of 1.412 and 3.984 respectively, as well as discriminant function analysis (percentage accuracy after cross validation = 66.9%). Conclusion: Among the sample population in Kaduna State, Nigeria, there is sexual dimorphism in some of the craniofacial variable found in CT scan images. Multivariate logistic regression may be the best model to utilize for predicting sex among the Kaduna State sample group.

Funder

NIL

Publisher

Cukurova Medical Journal

Subject

General Earth and Planetary Sciences,General Environmental Science

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