INTERNATIONAL JOURNAL OF MORPHOLOGY, cilt.41, sa.3, ss.749-757, 2023 (SCI-Expanded)
The study purposed to examine the morphometry and morphology of crista galli in cone beam computed
tomography (CBCT) and apply a new analysis, supervised Machine Learning techniques to find the answers to research questions
“Can sex be determined with crista galli morphometric measurements?” or “How effective are the crista galli morphometric
measurements in determining sex?”. Crista galli dimensions including anteroposterior, superoinferior, and laterolateral were measured
and carried out on 200 healthy adult subjects (98 females; 102 males) aged between 18-79 years. Also, crista galli was classified with
two methods called morphological types and Keros classification. In this study, the Chi-square test, Student's t-test, and Oneway
ANOVA were performed. Additionally, Machine Learning techniques were applied. The means of the CGH, CGW, and CGL were
found as 14.96 mm; 3.96 mm, and 12.76 mm in males, respectively. The same values were as 13.54 mm; 3.51 mm and 11.59±1.61 mm
in females, respectively. The CG morphometric measurements of males were higher than those of females. There was a significant
difference between sexes in terms of morphological classification type. Also, when the sex assignment of JRip was analyzed, out of
102 male instances 62 of them were correctly predicted, and for 98 female instances, 70 of them were correctly predicted according to
their CG measurements. The JRip found the following classification rule for the given dataset: “if CGH<=14.4 then sex is female,
otherwise sex is male”. The accuracy of this rule is not high, but it gives an idea about the relationship between CG measurements and
sex. Although the issue that CG morphometric measurements can be used in sex determination is still controversial, it was concluded
in the analysis that CG morphometric measurements can be used in sex determination. Also, Machine Learning Techniques give an
idea about the relationship between CG measurements and sex.