Open Access
CC BY 4.0 · European Journal of General Dentistry
DOI: 10.1055/s-0045-1808231
Original Article

Discriminant Function Analysis for Gender Determination Using Maxillary Sinus Volume and Index: A Cone-Beam Computer Tomography Study

Nuraddeen Ibrahim Jaafar
1   Department of Biomedical Sciences, College of Dentistry, King Faisal University, Al-Ahsa, Saudi Arabia
,
Prashanth Kumar Katta
2   Department of Restorative Dental Sciences, College of Dentistry, King Faisal University, Al-Ahsa, Saudi Arabia
› Author Affiliations
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Abstract

Objective

The purpose of the study was to establish baseline values for maxillary sinus volume (MSV) and maxillary sinus index (MSI) among a population of Saudi adults and to test the accuracy of using MSV and MSI for gender determination among the study samples.

Materials and Methods

Two hundred and seventy-five cone-beam computer tomographic (CBCT) images of adults that satisfy the study criteria were recruited as the study samples. The CBCT images were obtained from the patients' records in the CBCT database of the dental clinic of King Faisal University, Al-Ahsa, Saudi Arabia. Maxillary sinus height (H), width (W), and depth (D) were measured from the coronal, axial, and sagittal image views using a vision iCAT software. The MSV and MSI were obtained by the mathematical formula MSV = H*W*D*0.33 in cm3 and MSI = W/H. Data was analyzed using SPSS software, and a discriminant function test was used to test for gender determination.

Results

Mean MSV was 12.67 mm ± 3.46, and mean MSI was 0.88. No statistically significant difference was observed between the right and left (p > 0.05). There was strong gender dimorphism in both MSV and MSI in the samples. Males have statistically higher mean (p = 0.001) MSV and MSI (p = 0.002). The discriminant function test shows an overall gender predictive accuracy of 84% when MSV was used for gender determination and 77% when MSI was used.

Conclusion

Both MSV and MSI are good predictors for gender determination, but MSV is superior and has higher accuracy.



Publication History

Article published online:
28 April 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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