Crista Galli Morphometry and Morphology: An Anatomical, Radiological, and Machine Learning Application Study

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