Gender Determination from Digital Lateral Cephalogram for an Adolescent and Adult Cohort from a Pondicherry Population – A Cross-Sectional Study

Abstract

Background: Identifying sex from a skull is a test for a forensic odontologist. There are research done to determine sex based on cranial values. The aim of the present research is to assess the role of facial skeletal traits in digital lateral cephalogram to determine gender. Objectives: To derive a discriminant function equation to determine gender for an adolescent and adult cohort. Methods and Material: From the archive of the Institution, 98 digital lateral cephalograms of 52 males and 46 females with an age range from 13 to 55 years were retrieved to measure 12 facial skeletal variables using DIMAXIS-3 software. Discriminant function analysis was used to analyze the data. Results: On initial discriminant function analysis, Ramus height (RH), lower facial height, mandibular length, and anterior cranial base (SN length) were found to be better predictors. On subsequent stepwise discriminant function analysis, RH only appeared to detect the gender with utmost accuracy when all 12 variables were included in the analysis. Conclusions: RH is the better predictor of gender of all the parameters taken in this study on a Pondicherry population.

Keywords

• analysis
• cephalometry
• discriminant
• gender