Sex estimation standards for medieval and contemporary Croats

D, Ramani P, Arafat A, Khairy A. SRY gene isolation from teeth for forensic gender identification-An observational study

Personal identification in forensics is possible with gender determination using DNA (deoxyribonucleic acid) analysis. DNA isolation from teeth samples subjected to extreme temperatures has been shown to predict the gender of the deceased. However, the literature lacks studies on DNA extracted from tooth samples exposed to freezing temperatures. This study aimed to isolate the SRY gene from the extirpated pulp of teeth that were subjected to varying temperatures for gender identification. Thirty teeth with vital pulps, divided into 3 groups were included in the study. Each group consisted of 5 male and 5 female tooth samples. The groups were exposed to diverse environmental factors for three weeks. Group 1: room temperature (R group); Group 2: high temperature (H group) and Group 3: freezing temperature (F group). Later, DNA was isolated from the pulp tissue, and the SRY gene was amplified using PCR (Polymerase Chain Reaction). The Sensitivity and Specificity of the results were analyzed. SRY gene detected in the study samples identified accurate gender with a 46.70% Sensitivity and 93.30% Specificity. Significant difference was found in the correlation between gene expression and gender among the three groups (p = 1.000). The study validates that dental pulp tissue can be a reliable source for DNA extraction. And SRY gene amplification from teeth exposed to diverse environmental conditions. Further investigations are required to validate its application in forensics.

Sex estimation using orbital measurements in the Croatian population

The aim of this study was to test the sexual dimorphism of orbital measurements in the Croatian population using multi-slice computed tomography (MSCT) images. We have retrospectively taken 414 head CT scans of adults from Croatian clinical hospitals in Split and Zagreb (214 males and 200 females) with slice thickness < 1 mm and no pathological or traumatic changes that could affect the measurements. DICOM files were imported into Stratovan Checkpoint Software and viewed in 2D and 3D using semi-transparent 3D volume rendering. Eight standard measurements were calculated based on twelve orbital landmarks (six paired). Principal component analysis (PCA) was used to explore sexual and regional differences, and linear discriminant analysis was used to develop sex classification models. The PCA showed separation based on sex and region, and additional analysis demonstrated that females and males in Split and Zagreb differed in four orbital measurements (P ≤ 0.001). Only those measurements that did not show regional differences were further analyzed, and all showed statistically significant sexual dimorphism. The accuracy of univariate functions for sex estimation ranged from 53.43 to 71.88%, and for multivariate function, the accuracy was 73.45%. The orbital measurements of the Croatian population showed restricted forensic significance for sex classification. On the other hand, we have shown that they can have a potential for exploring the inter- and intra-population differences.

Employing deep learning for sex estimation of adult individuals using 2D images of the humerus

Biological profile estimation, of which sex estimation is a fundamental first stage, is a really important task in forensic human identification. Although there are a large number of methods that address this problem from different bone structures, mainly using the pelvis and the skull, it has been shown that the humerus presents significant sexual dimorphisms that can be used to estimate sex in their absence. However, these methods are often too subjective or costly, and the development of new methods that avoid these problems is one of the priorities in forensic anthropology research. In this respect, the use of artificial intelligence may allow to automate and reduce the subjectivity of biological profile estimation methods. In fact, artificial intelligence has been successfully applied in sex estimation tasks, but most of the previous work focuses on the analysis of the pelvis and the skull. More importantly, the humerus, which can be useful in some situations due to its resistance, has never been used in the development of an automatic sex estimation method. Therefore, this paper addresses the use of machine learning techniques to the task of image classification, focusing on the use of images of the distal epiphysis of the humerus to classify whether it belongs to a male or female individual. To address this, we have used a set of humerus photographs of 417 adult individuals of Mediterranean origin to validate and compare different approaches, using both deep learning and traditional feature extraction techniques. Our best model obtains an accuracy of 91.03% in test, correctly estimating the sex of 92.68% of the males and 89.19% of the females. These results are superior to the ones obtained by the state of the art and by a human expert, who has achieved an accuracy of 83.33% using a state-of-the-art method on the same data. In addition, the visualization of activation maps allows us to confirm not only that the neural network observes the sexual dimorphisms that have been proposed by the forensic anthropology literature, but also that it has been capable of finding a new region of interest.

Cranial and Odontological Methods for Sex Estimation—A Scoping Review

The estimation of sex from osteological and dental records has long been an interdisciplinary field of dentistry, forensic medicine and anthropology alike, as it concerns all the above mentioned specialties. The aim of this article is to review the current literature regarding methods used for sex estimation based on the skull and the teeth, covering articles published between January 2015 and July 2022. New methods and new approaches to old methods are constantly emerging in this field, therefore resulting in the need to summarize the large amount of data available. Morphometric, morphologic and biochemical analysis were reviewed in living populations, autopsy cases and archaeological records. The cranial and odontological sex estimation methods are highly population-specific and there is a great need for these methods to be applied to and verified on more populations. Except for DNA analysis, which has a prediction accuracy of 100%, there is no other single method that can achieve such accuracy in predicting sex from cranial or odontological records.

Sex estimation of skeletons in middle and late adulthood: reliability of pelvic morphological traits and long bone metrics on an Italian skeletal collection

There are several metric and morphological methods available for sex estimation of skeletal remains, but their reliability and applicability depend on the sexual dimorphism of the remains as well as on the availability of preserved bones. Some studies showed that age-related changes on bones can cause misclassification of sex. The purpose of this study was to establish the reliability of pelvic morphological traits and metric methods of sex estimation on relatively old individuals from a modern Italian skeletal collection. The data for this study were obtained from 164 individuals of the Milano CAL skeletal collection and average age of the samples was 75 years. In the pelvic morphological method, the recalibrated regression formula of Klales and colleagues (2012), pre-auricular sulcus, and greater sciatic notch morphology were used for sex estimation. With regard to the metric method, 15 standard measurements from upper and lower limbs were analyzed for sexual dimorphism. The results showed that in pelvic morphological approach, the application of regression formula of the revised Klales and colleague formula (2017) resulted in 100% accuracy. Classification rates of metric methods vary from 75.19 to 90.73% with the maximum epiphyseal breadth of proximal tibia representing the most discriminant parameter. This study confirms that the effect of age on sex estimation methods is not substantial, and both metric and morphological methods of sex estimation can be reliably applied to individuals of Italian descent in middle and late adulthood.

Adjusting posterior probabilities to meet predefined accuracy criteria: A proposal for a novel approach to osteometric sex estimation

The osteometric methods are the most reliable way to estimate the sex of skeletons when DNA analysis is not used. However, as osteometric studies usually ignore the overlap in male and female skeletal dimensions, they rarely achieve accuracy sufficient for forensic application. To resolve this issue, recent studies suggest sex estimation only when posterior probability (pp) is greater than 0.95, but that approach does not always provide sufficient accuracy and creates a large proportion of unsexed skeleton. Thus, our study aimed to explore whether it is possible to adjust pp on skeletal measurements with pronounced sexual dimorphism to meet 95% accuracy and to enable sex estimation on a reasonable proportion of individuals. From 207 skeletons, we included 65 postcranial measurements and selected 10% of variables with the highest sexual dimorphism. We computed univariate and bivariate discriminant functions using pp threshold of 0.5, 0.95, and the threshold required to achieve accuracy of ≥ 95%. Discriminant functions with pp=0.5 obtained accuracy of 85%-93%, while those with pp≥0.95 and adjusted posterior probabilities obtained 94%-99%. However, we showed that by selecting a particular threshold, sex could be estimated on a greater proportion of individuals than for pp≥0.95: 42%-86% vs. 24%-62% for univariate and 69%-95% vs. 49%-78% for bivariate functions. Therefore, when developing sex estimation models, we suggest not to use fixed pp level, but to adjust pp to achieve 95% accuracy and to minimize the percentage of unsexed skeletons.