Correlation between Facial Type and Certain Radiomorphometric Facial Indices according to Gender Differences in Each Type among Iraqi Adult using Cone Beam Computed Tomography

Background The human face, with its bone and muscle structures, presents its special characteristics. It may be categorized into essentially three kinds in the horizontal and vertical directions. One form of categorization, which takes into account the vertical plane of the face separates it into short or brachyfacial, medium or mesofacial and long or dolichofacial. The aim of the current study to describe several orofacial indexes and proportions of adults, according to gender difference and to verify the possibility of establishing a form of facial classification based on anthropometry in Iraqi subjects by using CBCT. Material and methods This prospective study included 100 Iraqi patients (males and females) ranging from 20 to 40 years. All subjects attended the Oral and Maxillofacial Radiology Department of Health Specialist Center for Dentistry in AL Sadr city in Baghdad taking CBCT scan for different diagnostic purposes from October 2016 to May 2017. The facial index was used for determination of facial type. Subjects were divided according to gender and facial type, and then calculated five variables inferior face index, superor face index, chin-face proportion, chin proportion and mandibular proportion. Results The subjects divided according to facial type that presented significant differences were chin face proportion, chin proportion and mandibular proportion for dolichofacial type and superior face index for mesofacial type. To predict facial types, the following parameters was considered significant inferior face index and superior face index for the dolichofacial type for males and the inferior face index, superior face index and mandibular proportion for the dolichofacial type for females. Conclusion There are variation in some indexes and proportions according to gender differences. The variables in the current study are not good predictors for facial types determination.