Evaluation of Greater Sciatic Notch Parameters in Sex Determination of Hip Bone by Three-Dimensional CT Images
Journal Title: Journal of Clinical and Diagnostic Research - Year 2018, Vol 12, Issue 9
Abstract
ABSTRACT Introduction: Sex determination of an anonymous individual is one of the main objective when human skeletal remains are found, both in forensic investigation and archaeological studies. Aim: To evaluate the role of Greater Sciatic Notch (GSN) parameters in sex determination in the Iranian population by means of Three-Dimensional (3D) images reconstructed by multi-slice Computed Tomography (CT). Materials and Methods: In the present cross-sectional study, 237 cases (121 females and 116 males) who received Pelvic CT in radiology department of Rasoul-e-Akram Hospital were included. The GSN parameters including the width, depth and posterior segment were applied to measure the 3D-CT radiographs of participant's hip bone using digital instruments with an accuracy of 0.01º and 0.01 mm. SPSS version 21 was used to analyse the data using the independent sample t-test, chi-square test, Pearson’s correlation test and Roc curve. Results: Among the GSN parameters, depth had no difference among males and females, in both right and left sides (p=0.767 and p=0.561, respectively); thus, was not useful in sex determination. GSN parameters including Depth (p=0.008), Post segment (p=0.017), and Index 2 (p=0.015) were different in right and left sides and cannot be considered for sex determination without considering the sides. Moreover, Post angle (90.3%) and Post segment (89.5%) were found to have the most accuracy in sex determination. Conclusion: Most of the parameters of GSN except for depth were useful for sex determination. Application of 3D-CT micrographs in the present study helped us to easily quantify sexual dimorphism in the GSN, suggesting 3D-CT can be considered as one of the valuable tools in practical forensic osteology investigation due to the great accuracy to measure the sex differences.
Authors and Affiliations
Siamak Soltani, Maryam Ameri, Kamran Aghakhani, Soheila Ghorbani
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