Mandibular Sexual Dimorphism Analysis in CBCT Scans in a Syrian Sample
Abstract
Objectives: This study aimed to develop metrical and angular indices to predict the sex of unknown skeletonized human remains in Syria using the mandibular bone.
Material and Methods: The sample consisted of 99 CBCT scans of the Syrian population (43 males, 56 females) aged between 18-25 years. The collected CBCT images were analyzed on two occasions and by two examiners to test the reliability of measurements. Four measurements were analyzed to be used for sexual prediction analysis as following: Coronoid-Gonion length (the distance between Gonion and the highest lateral point on the Coronoid process (, minimum ramus breadth, Gonial angle, and Bigonial width.
Results: All the aforementioned measurements showed significant statistical sex-related differences. The Bigonial width showed the highest difference with (P < 0.01 _ mean: 95.17 ± 6.45 mm for males, 86.84 ± 4.81 mm for females), followed by the gonial angle (P<0.01_ mean: 127.11º ± 7.87º for males, 131.52º ± 6.08º for females). Coronoid-gonion length (P < 0.01 _ mean: 58.61 ± 6.78 mm for males, 53.97 ± 5.26 mm for females) and minimum ramus breadth (P < 0.01 _ mean: 29.63 ± 2.90 mm for males, 27.89 ± 2.73 mm for females). specificity and sensitivity for the four indices derived function were 79 % and 80 % respectively and the diagnostic accuracy was 79.6 %.
Conclusions: The present study suggested that the sex of mandible in Syrian population could be assessed using metrical and angular measurements which considered as an additional tool for sex identification.
Keywords
Supporting Institution
References
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Details
Primary Language
English
Subjects
Health Care Administration
Journal Section
Research Article
Authors
Publication Date
June 30, 2020
Submission Date
January 23, 2020
Acceptance Date
May 6, 2020
Published in Issue
Year 2020 Volume: 23 Number: 2
Cited By
The Accuracy of Sex Identification Using CBCT Morphometric Measurements of the Mandible, with Different Machine-Learning Algorithms—A Retrospective Study
Diagnostics
https://doi.org/10.3390/diagnostics13142342