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Evaluation of Relation of Dimensional Measurement of Differe | 1424

Journal of Research in Medical and Dental Science
eISSN No. 2347-2367 pISSN No. 2347-2545

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Evaluation of Relation of Dimensional Measurement of Different Anatomic Skull Structures to Determine Sexual Dimorphism in Cone Beam CT Images of an Iranian Population

Author(s): Ramin Nourbakhsh, Sedighe Razi, Tahmine Razi

Abstract

Several bones have been examined for identification and determining the sex in the corpses, such as skull bones. Nowadays, CBCT images have high importance, since they have ability to produce images in different designs, which increase the efficiency of diagnosis. Thus, the objective of this research was to evaluate the dimensional measurement of different anatomic skull structures in determining the sexual dimorphism accuracy in cone beam CT images in an Iranian population. In this descriptive-analytical study, 102 patients, admitted to Oral and Maxillofacial Radiology Department of Tabriz Faculty of Dentistry for evaluation by CBCT were examined. For this purpose, landmarks of frontal and maxillary sinus, mandible and foramen magnum were measured and finally analyzed by SPSS software to determine the sexual dimorphism accuracy of the measurements. The results of this research revealed that in terms of difference between males and females, the greatest difference belonged to different parts of the maxillary sinus. Results of different parts of the right and left frontal sinus in this research revealed no difference among them. Moreover, the sexual dimorphism accuracy from frontal sinus was estimated about 76.7% in this research. Images related to CBCT of frontal sinus, maxillary sinus, and mandibular landmarks can be helpful in sex determination. The results of sex determination accuracy by landmarks measured in this research revealed that the highest sex determination accuracy belonged to size of ramus height with 81%, and the lowest accuracy belonged to total width of the frontal sinus with 45%. In addition, considering all the variables at the same time, the predictive power was found 100%.

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