Radiometry versus osteometry in sex assessment: a study of the Cretan radius

A web application for sex and stature estimation from radiographic proximal femur for a Thai population

In both forensic and archaeological domains, the discovery of incomplete human remains is a frequent occurrence. Nevertheless, the estimation of biological profiles from such remains presents a challenge due to the absence of crucial skeletal elements, such as the skull and pelvis. This study aimed to assess the utility of the proximal femur in the forensic identification process by creating a web application for osteometric analysis of the proximal femur. The aim was to determine the sex and stature of an individual from radiographs of the left anteroposterior femur. To accomplish this, an automated method was developed for acquiring linear measurements from radiographic images of the proximal femur using Python tools. The application of Hough techniques and Canny edge detection was utilized to generate linear femoral dimensions from radiographs. A total of 354 left femora were radiographed and measured by the algorithm. The sex classification model employed in this study was the Naïve Bayes algorithm (accuracy = 91.2 %). Results indicated that Gaussian process regression (GPR) was the most effective method for estimating stature (mean error = 4.68 cm, SD = 3.93 cm). The proposed web application holds the potential to serve as a valuable asset in the realm of forensic investigations in Thailand, particularly in the estimation of biological profiles from fragmentary skeletal remains.

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.