Dental metric standards for sex estimation in archaeological populations from Iran

Sex estimation of skeletal remains is one of the major components of forensic identification of unknown individuals. Teeth are a potential source of information on sex and are often recovered in archaeological or forensic contexts due to their post-mortem longevity. Currently there is limited data on dental sexual dimorphism of archaeological populations from Iran. This dissertation represents the first study to provide a dental sex estimation method for Iron Age populations. The current study was conducted on the skeletal remains of 143 adults from two Iron Age populations in close temporal and geographic proximity in the Solduz Valley (West Azerbaijan Province of Iran). 2D and 3D cervical mesiodistal and buccolingual and root volume measurements of maxillary and mandibular teeth were used to investigate the degree of sexual dimorphism in permanent dentition and to assess their applicability in sex estimation. In total 1327, 457, and 480 anterior and posterior teeth were used to collect 2D cervical, 3D cervical, and root volume measurements respectively. 2D cervical measurements were taken using Hillson-Fitzgerald dental calliper and 3D measurements were collected using CT images provided by Open Research Scan Archive (ORSA) - Penn Museum. 3D models of the teeth were created using manual segmentation in the Amira 6.01 software package. Since tooth density largely differs from crown to apex, root segmentation required two threshold levels: the segmentation of the root from the jaw and the segmentation of the crown from the root. Thresholds used for root segmentation were calculated using the half maximum height protocol of Spoor et al. (1993) for each skull, and thresholds used for crown segmentation were set visually for each tooth separately. Data was analysed using discriminant function analysis and posterior probabilities were calculated for all produced formulae where sex was previously assessed from morphological features of pelvis and skull. Bootstrapping was used to account for small sample sizes in the analysis. Statistical analysis was carried out using SPSS 23. The percentage of sexual dimorphism was also used to quantify the amount of sexual dimorphism in the sample. The results showed that incisors and canines were the most sexually dimorphic teeth, providing percentages of correct sex classification between 80% and 100% depending on the measurement used. Root volume measurement was shown to be the most sexually dimorphic variable providing an accuracy of over 90% in all functions. The present study provided the first dental metric standards for sex estimation using odontometric data in Iranian archaeological populations. Dental measurements, particularly root volume measurements, were found to be of value for sex assessment and the method presented here could be a useful tool for establishing accurate demographic data from skeletal remains of the Iron Age from Iran

Back-Propagation neural network for gender determination in forensic anthropology

Determination of gender is the foremost and important step of forensic anthropology in determining a positive identification from unidentified skeletal remains. Gender determination is the classification of an individual into one of two groups, male or female. The classification technique most used by anthropologists or researchers is traditional gender determination with applied linear approach, such as Discriminant Function Analysis (DFA). This paper proposed non-linear approach specific Back-Propagation Neural Network (BPNN) to determine gender from sacrum bone. Sacrum bone is one part of the body that is usually regarded as the most reliable indicator of sex. The data used in the experiment were taken from previous research, a total of 91 sacrum bones consisting of 34 females and 57 males. Method of measurement used is metric method which is measured based on six variables; real height, anterior length, anterior superior breadth, mid-ventral breadth, anterior posterior diameter of the base, and max-transverse diameter of the base. The objective of this paper is to examine and compare the degree of accuracy between previous research (DFA) and BPNN. There are two architectures of BPNN built for this case, namely [6; 6; 2] and [6; 12; 2]. The best average accuracy obtained by BPNN is model [6; 12; 2] with accuracy 99.030 % for training and 97.379 % for testing on experiment lr = 0.5 and mc = 0.9, then obtained Mean Squared Error (MSE) training is 0.01 and MSE testing is 1.660. Previous research using DFA only obtained accuracy as high as 87 %. Hence, it can be concluded that BPNN provide classification accuracy higher than DFA for gender determination in forensic anthropology

Medical imaging and facial soft tissue thickness studies for forensic craniofacial approximation: a pilot study on modern Cretans

Forensic cases may require craniofacial approximations for unidentifiable victims. The accuracy of these approximations is improved by using population-specific average soft tissue depths. This study used CT scans from 64 Cretan adults (32 male and 32 female) to produce three-dimensional models of each individual’s cranium and skin surface. Using the models, the soft tissue depths were measured at 36 craniofacial landmarks; the means and standard deviations were calculated for the general Cretan population, and for male and female Cretans separately. Cretan facial soft tissue depths were then compared to those of French, Slovak, and Korean adults. 16 of the 36 landmarks exhibited sex differences among Cretans, with males having consistently thicker depths than females. The facial soft tissue depths of Cretan adults also presented significant differences when compared to other populations. Overall, the average soft tissue depths obtained represent the first database for the craniofacial approximation of Cretan (Greek) adults