Sex estimation by Mastoid Triangle using 3D models

Sex Estimation From Measurements of the Mastoid Triangle and Volume of the Mastoid Air Cell System Using Classical and Machine Learning Methods: A Comparative Analysis

ABSTRACT

Previous studies on the sexual dimorphism of the mastoid triangle have typically focused on linear and area measurements. No studies in the literature have used mastoid air cell system volume measurements for direct anthropological or forensic sex determination. The aims of this study were to investigate the applicability of mastoid air cell system volume measurements and mastoid triangle measurements separately and combined for sex estimation, and to determine the accuracy of sex estimation rates using machine learning algorithms and discriminant function analysis of these data. On 200 computed tomography images, the distances constituting the edges of the mastoid triangle were measured, and the area was calculated using these measurements. A region-growing algorithm was used to determine the volume of the mastoid air cell system. The univariate sex determination accuracy was calculated for all parameters. Stepwise discriminant function analysis was performed for sex estimation. Multiple machine learning methods have also been used. All measurements of the mastoid triangle and volumes of the mastoid air cell system were higher in males than in females. The accurate sex estimation rate was determined to be 79.5% using stepwise discriminant function analysis and 88.5% using machine learning methods.

Comparative study of sex estimates in adult skulls using direct measurement and tomographic image reconstruction

Sex estimation is an important procedure in forensic anthropology for human identification. The development of new technologies, such as three-dimensional computed tomography (CT), has provided excellent alternatives for this purpose. This study examined and compared a morphological method for sex estimation using two different approaches - direct measurement of physical structures and tomographic analysis using 3D images. A total of 111 skulls from the Museum of Human Anatomy at the University of São Paulo (MAH-USP) were used, (60 males and 51 females). All specimens were scanned by Philips Brilliance 64 CT scanner equipment and their corresponding images were reconstructed in three-dimensional (3D) models. The morphological characteristics of the skulls were analyzed by an observer who was blinded to the sex of the specimens. Five cranial structures were analyzed: external occipital crest, mastoid process, supraorbital margin, glabella, and mental eminence. The structures were scored 1 to 5 according to Buikstra and Ubelaker and validated by Walker. The success rates of the sex estimates obtained through direct measurement of the dry skulls ranged from 67.4% to 70.4% as compared to 60.2% to 68.1% for CT reconstruction. When analyzed separately, the maximum accuracy of the method was 68.33% in males and 88.24% in females in the physical analysis of structures. The glabella and mastoid process were the most effective structures to estimate sex through both techniques, respectively. Our results show that 3D CT images can be accurately used in the morphological analysis for sex estimation, representing a viable alternative in forensic anthropology.

Anatomy of the mastoid triangle and morphometric sex differences

OBJECTIVE OF THE STUDY

This study aimed to quantify the area of the mastoid triangle (MT) and assess potential morphometric differences between males and females.

PATIENTS

The sample consisted of 244 dry human skulls, with biological sex known based on genetic analysis, collected from a medicolegal osteological database from Central-Western Brazil.

MATERIALS AND METHODS

The study was observational, analytical, and cross-sectional. The skulls were analyzed using Heron's equation to calculate the area of the MT. The landmarks connecting each of the sides of the triangle were: Porion (Po)>Mastoidale (Ma)>Asterion (Ast). Morphometric references were calculated and compared based on sex.

RESULTS

The area of the MT was nearly 14% larger in males compared to females (p<0.05). The mean MT area for the right and left sides of males were 684.11±93.25mm² and 668.94±111.95mm², respectively. In females, the mean MT for the right and left sides were 588.93±91.09mm² and 582.88±102.98mm², respectively. Right and left side measurements were significantly different (p<0.05), except for Po-Ast (p=0.232).

CONCLUSION

Morphometric features regarding the MT were slightly different between males and females. Application of the MT as a dimorphic tool should be adjuvant. Moreover, this tool should be considered carefully, especially because the sex-based differences were statistically significant, but discrete between males and females.

Radiographic Evaluation of Mastoid Parameters for Sexual Differentiation in North Indian Population

Introduction: Morphometric differences of several bones form the basis of sexual differentiation. The mastoid triangle has been widely used as a predictor of sexual differentiation. However, the radiographic measurements of the mastoid triangle, which form the clinical alternative of this parameter, have not been studied in the North Indian population. Therefore, we analyzed skull radiographs of live subjects to investigate the effectiveness of the radiographic mastoid triangle in sex determination.

Methods: One hundred skull lateral radiographs (55 male and 45 female) from the digital archives of a tertiary care teaching institute in Northern India were retrospectively analyzed. The following parameters: porion-mastoidale length, mastoidale-asterion length, asterion-porion length, and area of the mastoid triangle were measured. Gender-based differences for these parameters were then calculated for any statistical significance. Further, the low value of Wilks’ lambda, high values of Eigenvalues, and percentage of correct prediction accuracy denoted higher predictive value. Finally, discriminant function analysis was used to predict the relative validity of each measured parameter.

Result: All measured parameters were significantly higher in the male group. The porion-mastoidale length was 32.21±2.15 mm in males and 31.66±3.21 mm in females. The mastoidale-asterion length was 50.00±9.75 mm in males and 49.84±6.97 in females. The asterion-porion length was 44.11±6.82 mm in males and 39.72±5.77 mm in females. The area of the mastoid triangle was 690.74±123.35 mm² in males and 570.57±130.0 mm² in females. The area of the mastoid triangle has the highest relative validity (78%).

Conclusion: Considerable ethnic and racial differences have been observed in the radiographic morphology of the mastoid. The radiographic dimensions of the mastoid are potential predictors of sexual dimorphism. With the use of discriminant function analysis, the current study predicts the effectiveness of the area of the mastoid triangle as a reliable parameter for sexual differentiation in the Northern Indian population.

Machine learning approaches for sex estimation using cranial measurements

The aim of the present study is to apply support vector machines (SVM) and artificial neural network (ANN) as sex classifiers and to generate useful classification models for sex estimation based on cranial measurements. Besides, the performance of the generated sub-symbolic machine learning models is compared with models developed through logistic regression (LR). The study was carried out on computed tomography images of 393 Bulgarian adults (169 males and 224 females). The three-dimensional coordinates of 47 landmarks were acquired and used for calculation of the cranial measurements. A total of 64 measurements (linear distances, angles, triangle areas and heights) and 22 indices were calculated. Two datasets were assembled including the linear measurements only and all measurements and index, respectively. An additional third dataset comprising all possible interlandmark distances between the landmarks was constructed. Two machine learning algorithms-SVM and ANN and a traditional statistical analysis LR-were applied to generate models for sex estimation. In addition, two advanced attribute selection techniques (Weka BestFirst and Weka GeneticSearch) were used. The classification accuracy of the models was evaluated by means of 10 × 10-fold cross-validation procedure. All three methods achieved accuracy results higher than 95%. The best accuracy (96.1 ± 0.5%) was obtained by SVM and it was statistically significantly higher than the best results achieved by ANN and LR. SVM and ANN reached higher accuracy by training on the full datasets than the selection datasets, except for the sample described by the interlandmark distances, where the reduction of attributes by the GeneticSearch algorithm improved the accuracy.

Data mining for sex estimation based on cranial measurements

The aim of the present study is to develop effective and understandable classification models for sex estimation and to identify the most dimorphic linear measurements in adult crania by means of data mining techniques. Furthermore, machine learning models and models developed through logistic regression analysis are compared in terms of performance. Computed tomography scans of 393 adult individuals were used in the study. A landmark-based approach was applied to collect the metric data. The three-dimensional coordinates of 47 landmarks were acquired and used for calculation of linear measurements. Two datasets of cranial measurements were assembled, including 37standard measurements and 1081 interlandmark distances, respectively. Three data mining algorithms were applied: the rule induction algorithms JRIP and Ridor, and the decision tree algorithm J48. Two advanced attribute selection methods (Weka BestFirst and Weka GeneticSearch) were also used. The best accuracy result (91.9 %) was achieved by a set of rules learnt by the JRIP algorithm from the dataset constructed by application of the GeneticSearch selection algorithm to the dataset of standard cranial measurements. The set consisted of five rules including seven cranial measurements. Its accuracy was even better than the classification rates achieved by the logistic regression models. Concerning the second dataset of nonstandard measurements, the best accuracy (88.3 %) was obtained by using classification models learnt by two algorithms - JRIP with a dataset preprocessed by the BestFirst selection algorithm and Ridor with preprocessing by the GeneticSearch selection algorithm. Our experiments show that for the two datasets mentioned above the rule-based models contain smaller sets of rules with shorter lists of measurements and achieve better classification accuracy results in comparison with decision tree-based models.