Sex estimation from the clavicle using 3D reconstruction, discriminant analyses, and neural networks in an Eastern Turkish population

Abstract

Sex estimation of skeletal remains is an important aspect of forensic anthropology. The clavicle is a bone with relatively high accuracy in sex determination. In this study, 7 clavicular parameters were obtained using the CT images and 3D reconstruction of 360 cases equally distributed as 180 males and 180 females. Sex determination was made using univariate, linear, and stepwise discriminant analyses, and multilayer perceptron neural networks. Maximum sex determination accuracy of 85.3% was achieved with univariate analysis, 89.4% with linear discriminant analysis, 90.0% with stepwise discriminant analysis, and 91.4% with multilayer perceptron neural networks. Significant changes were observed in the MLC, APMD-R and CDC parameters according to age, and these were determined to affect the accuracy of sex determination in different age groups. In forensic anthropological studies, more reliable results can be obtained by considering the confounding factors during sampling. Although high accuracy rates can be achieved with neural networks, the results should be approached with caution.

Keywords: Clavicle; Discriminant function analysis; Neural networks; Sex estimation; Stepwise discriminant analysis.