Advancements in sex estimation using the diaphyseal cross-sectional geometric properties of the lower and upper limbs.

This paper introduces an automated method for estimating sex from the lower and upper limbs based on diaphyseal CSG properties. The proposed method was developed and evaluated using 389 femurs, 412 tibias, and 404 humeri of adult individuals from a modern Greek reference sample, the Athens Collection. The skeletal properties, which were extracted with the CSG-Toolkit, were analyzed with step-wise DFA (evaluated with LOOCV) and subsequently with RBF kernel SVM supervised learning. SVM cross-validation was based on a 20-fold stratified random sample splitting as well as a chronological split based on year of birth to further assess the effect of secular change in sex estimation capacity. Maximum cross-validated classification accuracy from step-wise DFA reached 94.8% for the femur, 94.7% for the tibia, and 97.3% for the humerus, whereas SVM cross-validated results were similar although slightly lower, mainly due to the more strict cross-validation scheme. Our results suggest that the proposed sex estimation method is reasonably robust to secular change, since there was limited loss in classification accuracy between different chronological groups, despite the presence of secular change in stature of the Greek population during the examined period. The proposed method has been implemented as a function for the GNU Octave environment, named estimate_sex, which comprises a self-intuitive graphical user interface for facilitating sex estimation and is freely available under a suitable license.