Testing landmark redundancy for sex-based shape analysis of the adult human os coxa

Sex assessment using the radius bone in a French sample when applying various statistical models

Sex estimation of skeletal remains is one of the most important tasks in forensic anthropology. The radius bone is useful to develop standard guidelines for sex estimation across various populations and is an alternative when coxal or femoral bones are not available.The aim of the present study was to assess the sexual dimorphism from radius measurements in a French sample and compare the predictive accuracy of several modelling techniques, using both classical statistical methods and machine learning algorithms.A total of 78 left radii (36 males and 42 females) were used in this study. Sixteen measurements were made. The modelling techniques included a linear discriminant analysis (LDA), flexible discriminant analysis (FDA), regularised discriminant analysis (RDA), penalised logistic regression (PLR), random forests (RF) and support vector machines (SVM).The different statistical models showed an accuracy of classification that is greater than 94%. After selection of variables, the accuracies increased to 97%. The measurements made at the proximal part of the radius (sagittal and transversal diameters of the head, and sagittal diameter of the neck), at distal part (maximum width of the distal epiphysis) and of the entire bone (maximum length) stand out among the various models.The present study suggests that the radius bone constitutes a valid alternative for sex estimation of skeletal remains with comparable classification accuracies to the pelvis or femur and that the non-classical statistical models may provide a novel approach to sex estimation from the radius bone. However, the extrapolation of the current results cannot be made without caution because our sample was composed of very aged individuals.

Sex estimation from the greater sciatic notch: a comparison of classical statistical models and machine learning algorithms

The greater sciatic notch (GSN) is a useful element for sex estimation because it is quite resistant to damage, and thus it can often be assessed even in poorly preserved skeletons. This study aimed to develop statistical models for sex estimation based on visual and metric analyses of the GSN, and additional variables linked to the GSN. A total of 60 left coxal bones (30 males and 30 females) were analysed. Fifteen variables were measured, and one was a morphologic variable. These 16 variables were used for the comparison of six statistical models: linear discriminant analysis (LDA), regularized discriminant analysis (RDA), penalized logistic regression (PLR) and flexible discriminant analysis (FDA), and two machine learning algorithms, support vector machine (SVM) and artificial neural network (ANN). The statistical models were built in two steps: firstly, only with the GSN variables (group 1), and secondly, with the whole variables (group 2), in order to see if the models including all the variables performed better. The overall accuracy of the models was very close, ranging from 0.92 to 0.97 using specific GSN variables. When additional variables starting from the deepest point of GSN are available, it is worth to use them, because the accuracy increases. PLR (after optimization of parameters) stands out from other statistical models. The position of the deepest point of GSN (Fig. 2) probably plays a crucial role for the sexual dimorphism, as stated by the good performance of the visual assessment of this point and the fact that the A2 angle (posterior angle with the deepest point of the GSN as the apex) is included in all models.