Testing the accuracy of the SexEst software for sex estimation in a modern Greek sample

Abstract

Sex estimation from human skeletal remains is fundamental in osteoarcheology and forensic anthropology. The increasing availability of reference skeletal collections across the world has allowed the development of morphological and metric methods for skeletal sex estimation, some of which may be implemented in specialized computer software. The present study aims to evaluate the freely available SexEst software, which utilizes cranial and postcranial measurements, and different classification models for sex estimation, on a contemporary Greek population comprising of 227 (126 males and 101 females) adult individuals. After the calculation of intra-observer error to assess the repeatability of the measurements, the proposed variables were tested for classification accuracy individually and in different combinations. Based on the results, the postcranial models outperformed the cranial ones in all cases and can be adequately applied on a Greek population sample. The light gradient boosting (LGB) algorithm yielded the highest correct classification rates when no missing values exist, while the linear discriminant analysis (LDA) models should only be used when dealing with missing data. The highest classification accuracy for a 0.65 posterior probability threshold was reached when utilizing a combination of postcranial variables (89.67%), while the lowest was achieved with the cranial measurement “Glabella-occipital length” (45.00%). The same models yielded the highest and lowest accuracy for a 0.5 probability threshold, with values of 92.96% and 67.73%, respectively. Combining variables yielded higher accuracies in both skeletal regions, suggesting that the software would be more helpful in cases of intact skeletons. The loss of classification accuracy due to population specificity further corroborates the need to include different ancestries in sex estimation software.