Search

• The hyoid bone is one of the bones in the human body that shows sexual dimorphism. • All hyoid measurements in the current study were higher in males than in females. • Discriminant function analysis revealed significant sexual dimorphism in the hyoid bone. • Machine learning algorithms are capable of estimating sex from the hyoid bone. The hyoid bone is one of the bones in the human body that shows sexual dimorphism. The anthropological and anthropometric characteristics that determine sexual dimorphism are influenced by demographic differences. The aim of this study was to investigate the rate of sexual dimorphism of the hyoid bone in the adult Eastern Turkish population from the examination of the 3D computed tomography images of 240 patients, using discriminant function analysis (DFA), support vector machines (SVM), and artificial neural networks (ANN). These evaluations were based on eight hyoid measurements that have been frequently used in previous CT studies. The results showed that all eight measurements were higher in males than in females (p = 0.000). It was determined that sex could be estimated accurately at up to 93.3 % using DFA, 93.8 % using SVM and 95.4 % using ANN. The maximum accuracy rate achieved to 94.2 % in males using SVM, and 95.8 % in females using ANN. These high rates of sexual dimorphism found using DFA, SVM, and ANN in this study indicate that characteristics of the hyoid bone can be utilized to determine sex in the Eastern Turkish population. [ABSTRACT FROM AUTHOR]

• The reliability of sex estimation from the foramen magnum is under debate. • Some previously obtained discriminant formulas gave reliable results on our sample. • For the area value, measurement directly with image processing is more appropriate. • Machine learning algorithms can be used successfully in sex estimation studies. Although many studies have been conducted using the foramen magnum for sex estimation, recent findings have indicated that the discriminant and regression models obtained from the foramen magnum may not be reliable. Artificial Neural Networks, was used as a classification technique in sex estimation studies on some other bones, did not used in sex estimation studies on the foramen magnum until now. The aim of this study was sex estimation on an Eastern Turkish population sample using foramen magnum measurements, discriminant analyses and Artificial Neural Networks. The study was performed on the CT images of a total of 720 cases, comprising 360 males and 360 females. For sex estimation, discriminant analysis and Artificial Neural Networks were used. The accuracy rate was 86.7% with discriminant analysis and when sex estimation accuracy was determined according to cases with posterior probabilities above 95%, the accuracy ranged from 0% to 33.3%. With the use of the discriminant formulas of 2 other studies, obtained from different Turkish samples, sex could be determined at a rate of 84.6%. Some formulas were found to be unsuccessful in sex estimation. Sex estimation accuracy of 88.2% was achieved with Artificial Neural Networks. In this study, it was found that sex could be determined to some extent with discriminant formulas from other samples from the same population, although some formulas were unsuccessful. With the use of image processing techniques and machine learning algorithms, better results can be obtained in sex estimation. [ABSTRACT FROM AUTHOR]

Sex determination is an important issue of anthropological and forensic sciences. Determination of sex is a priority issue for further analysis of unidentified ancient human remains, because all techniques of identification are markedly different for males and females. The present study provides sex determination using discriminant analysis from tibia measurements in an ancient Anatolian population. In this study, a total of 7 tibia measurements were taken from 123 adults of known sex (62 males and 61 females) in Medieval Dilkaya population (A.D. 10th century). Osteometric measurements included were the length, circumference of midshaft and minimum, transverse and sagittal diameters of midshaft and nutrient foramen levels. Data were analyzed by student t-test and discriminant analysis using Statistical Package for the Social Sciences (SPSS) version 13.0 program. Results showed that grouping due to sex differentiations was accurate by tibia metric values between 73.5% and 90.2% in Dilkaya population. The midshaft circumference was the best single discriminating variable and results of this study compare with other studies. It is suggested that discriminant formulas developed by tibia measurements in this study can be used for sex determination accurately on fragmentary skeletal remains in ancient Anatolian populations. [ABSTRACT FROM AUTHOR]

• The clavicle has relatively high sexual dimorphism. • Age may be a confounding factor in clavicle studies and should be considered during sampling. • With stratified sampling more reliable results could be achieved. • Variations in the accuracy of sex estimation from the clavicle were found in different age groups. • Relatively higher accuracy of sex determination can be obtained with Neural Networks. Sex estimation of skeletal remains is an important aspect of forensic anthropology. The clavicle is a bone with relatively high accuracy in sex determination. In this study, 7 clavicular parameters were obtained using the CT images and 3D reconstruction of 360 cases equally distributed as 180 males and 180 females. Sex determination was made using univariate, linear, and stepwise discriminant analyses, and multilayer perceptron neural networks. Maximum sex determination accuracy of 85.3% was achieved with univariate analysis, 89.4% with linear discriminant analysis, 90.0% with stepwise discriminant analysis, and 91.4% with multilayer perceptron neural networks. Significant changes were observed in the MLC, APMD-R and CDC parameters according to age, and these were determined to affect the accuracy of sex determination in different age groups. In forensic anthropological studies, more reliable results can be obtained by considering the confounding factors during sampling. Although high accuracy rates can be achieved with neural networks, the results should be approached with caution. [ABSTRACT FROM AUTHOR]

Sexual differences in the human skeleton have been well studied in many populations. Odontometric analysis of the human sexual variation has been less investigated and mostly derived from the dentition of extinct populations. Turkey is situated in a unique location where populations from different regions mixed with each other and created a rich gene pool. One might anticipate that modern Turkish population is composed of genes from the Balkans, Caucasus, Middle East, Iran and further as well as from ancient Romans, Byzantines, Arabs and Asiatic Turks. It is clear that contemporary Turks are a mixture of these extant and extinct people and ideal to consider it a representative study population. The purpose of this study is to analyze dental dimensions and sexual variation in living Turks and develop forensic techniques to identify human remains from the teeth when any other technique is not available or not reliable. The study is composed of Ankara University dental students (50 male and 50 female casts, average age of 21 years). Bucco-lingual breadths from 14 teeth (I1 through M2 of the maxilla and mandible) are taken from the left side and analyzed using the discriminant function statistics. An intraobserver error test did not indicate any statistically significant difference between any two measurements. Results of the study revealed that males exceeded females significantly (P<0.001) in dimensions. Coefficient of variation was most obvious in I1s and I2s of both jaws in both sexes. Stepwise discriminant function statistics suggested that upper C, and lower C and M2 are the most contributory teeth to the function. Additional formulae were calculated for situation in which only one or a fragmented jaw is available for identification. Overall accuracy of sex diagnosis ranged from 73 to 77%. In conclusion this research supports earlier studies that sexual dimorphism is population specific. While dental difference between the sexes in several human populations has been found highly dimorphic, it was not found so in Turks and accuracy of classification remained low at about 77%. The difficulty or the lack of dimorphism comes from male subjects. [Copyright &y& Elsevier]

Sex estimation is an essential step in the process of the identification of the skeletal remains in forensic anthropology since it reduces the number of possible matches by half. In this study, sex estimation with 21 sacral and coccygeal metric parameters obtained from Computerized Tomography images of a Turkish population which consists of 480 patients that are equalized according to their sexes and ages, is performed. Univariate discriminant analysis, linear discriminant function analysis, stepwise discriminant function analysis, and multilayer perceptron neural networks are used in this study. A maximum of 67.1% accuracy for univariate discriminant analysis, 82.5% for linear discriminant function analysis, 78.8% for stepwise discriminant function analysis, and 86.3% for multilayer perceptron neural networks, were achieved. Although it does not reach an acceptable accuracy rate of 95% or more for sacrum and coccyx, sex estimation with neural networks is a promising field of research in corpses where identification is otherwise not possible, and further studies with other bones and with new techniques might give useful information., (Copyright © 2019 Elsevier B.V. All rights reserved.)