A quantitative approach for sex estimation based on cranial morphology

Prioritizing a high posterior probability threshold leading to low error rate over high classification accuracy: the validity of MorphoPASSE software for cranial morphological sex estimation in a contemporary population

An increasing number of software tools can be used in forensic anthropology to estimate a biological profile, but further studies in other populations are required for more robust validation. The present study aimed to evaluate the validity of MorphoPASSE software for sex estimation from sexually dimorphic cranial traits recorded on 3D CT models (n = 180) from three populations samples (Czech, French, and Egyptian). Two independent observers performed scoring of 4 cranial traits (2 of them bilateral) in each population sample of 30 males and 30 females. The accuracy of sex estimation using traditional posterior probability threshold (pp = 0.5) ranged from 85.6% to 88.3% and overall classification error from 14.4% to 11.7% for both observers, and corresponds to the previously published values of the method. The MorphoPASSE method is also affected by the subjectivity of the observers, as both observers show agreement in sex assignment in 83.9% of cases, regardless of the accuracy of the estimates. Applying a higher posterior probability threshold (pp 0.95) provided classification accuracy of 97.9% and 93.3% of individuals (for observer A and B respectively), minimizing the risk of error to 2.1% and 6.7%, respectively. However, sex estimation can only be applied to 54% and 66% of individuals, respectively. Our results demonstrate the validity of the MorphoPASSE software for cranial sex estimation outside the reference population. However, the achieved classification success is accompanied by a high risk of errors, the reduction of which is only possible by increasing the posterior probability threshold.

Metric evaluation of the anterior nasal spine to estimate sex and population group in South African individuals

INTRODUCTION

The anterior nasal spine is a pointed, midline projection of the maxilla. This bony structure dictates the overlying soft tissues providing the phenotypic features of the nose and upper lip and determines the differences in the mid-face morphology. Little data is available on the metric features of the Anterior nasal spine (ANS). This study aimed to perform metric evaluations of the ANS of white and black South African males and females to ascertain if morphological variations exist and if the differences are viable for the use in sex and population identification.

MATERIALS AND METHODS

The sample included 100 CBCT images for each population and sex group. Linear and angular measurements of the ANS were recorded in both the sagittal and axial planes.

RESULTS

Classification decision trees (pruned) were fitted to ascertain the relationship between population group, sex and the ANS measurements including and excluding age. For population group, all the ANS measurements were statistically significant for females but in males, all the ANS measurements were significant when performed individually. However, when fitted to the classification tree, Sagittal 2 did not show any statistical significance. When considering sex, only 2 of the ANS measurements (Sagittal 2 and Axial 1) were found to be significant. The results did not differ significantly when comparing the decision trees including and excluding age.

CONCLUSIONS

White South African individuals presented with a longer ANS that produced a more acute angle whereas black South African individuals presented with a shorter ANS and a more obtuse angle. Additionally, males presented with a longer ANS compared to females. ANS measurements were found to be more relevant for population discernment than for sex.

Mandibular and dental measurements for sex determination using machine learning

The present study tested the combination of mandibular and dental dimensions for sex determination using machine learning. Lateral cephalograms and dental casts were used to obtain mandibular and mesio-distal permanent teeth dimensions, respectively. Univariate statistics was used for variables selection for the supervised machine learning model (alpha = 0.05). The following algorithms were trained: logistic regression, gradient boosting classifier, k-nearest neighbors, support vector machine, multilayer perceptron classifier, decision tree, and random forest classifier. A threefold cross-validation approach was adopted to validate each model. The areas under the curve (AUC) were computed, and ROC curves were constructed. Three mandibular-related measurements and eight dental size-related dimensions were used to train the machine learning models using data from 108 individuals. The mandibular ramus height and the lower first molar mesio-distal size exhibited the greatest predictive capability in most of the evaluated models. The accuracy of the models varied from 0.64 to 0.74 in the cross-validation stage, and from 0.58 to 0.79 when testing the data. The logistic regression model exhibited the highest performance (AUC = 0.84). Despite the limitations of this study, the results seem to show that the integration of mandibular and dental dimensions for sex prediction would be a promising approach, emphasizing the potential of machine learning techniques as valuable tools for this purpose.

Assigned sex estimation with the clavicle and scapula: A study in a Portuguese reference sample

The estimation of biological sex is a critical step in the assessment of the biological profile of an anonymous skeletonized individual. In certain recovery circumstances, the most dimorphic skeletal areas, such as the pelvis, are absent or fragmented; in that case, other bones of the skeleton, including the clavicle and scapula, can be used to predict sex. The purpose of this research is to generate new models for the estimation of sex with clavicular and scapular measurements using a study-sample of 129 individuals with clavicle (65 males and 64 females) and 112 individuals with scapula (50 males and 62 females) from the Lisbon Identified Skeletal Collection (Portugal). A decision tree classifier (C4.5) and logistic regression (LR) were employed to create univariable and multivariable sex prediction models. Accuracy under cross-validation of the classification models is high (up to 93.8%), with minimal bias (<5%), particularly in the multivariable models. The proposed LR models facilitate the probabilistic estimation of biological sex, accounting for the significant overlap in the expression of sexual dimorphism.

Enlarged occipital spur and craniofacial morphology: a cephalometric analysis

OBJECTIVE

This cross-sectional study aimed to investigate the association between the occipital spur length and craniofacial morphology in individuals with occipital spur (OS).

METHODS

The study included cephalometric images from 451 individuals (196 females, 255 males, age range was 9-84 years). The spur length and craniofacial characteristics were evaluated using cephalograms. Based on spur length, subjects were divided into two groups: the OS group (N = 209) and the enlarged occipital spur (EOS) group (N = 242). Descriptive statistics, Independent T-test, Mann-Whitney U test, chi-square test, Kruskal-Wallis test, and age- and sex-based stratified analyses were performed. The level of significance was set at p < 0.05.

RESULTS

Males had significantly larger spur length than females. Spur length was shorter in individuals under 18 than the groups over 18. After adjusting for gender and age, ramus height, mandibular body length, effective length of maxilla, effective length of mandible, anterior cranial base length, posterior cranial base length, anterior facial height, posterior facial height, facial height index, and lower anterior facial height had statistically significant differences between OS group and EOS group.

CONCLUSIONS

Males exhibit greater spur length than females. Patients under 18 had a shorter spur length than adults. Linear craniofacial measurements were found to be greater in subjects with EOS than the individuals with OS. The craniofacial growth and development of an individual might be associated with EOS. The causal relationship between EOS and craniofacial development requires further longitudinal studies.

A methodological comparison of discriminant function analysis and binary logistic regression for estimating sex in forensic research and case-work

The purpose of this study is to assess the accuracy of two multivariate statistical approaches for estimating sex from human external ear anthropometry, namely, discriminant function analysis (DFA) and binary logistic regression (BLR). A cross-sectional sample of 497 participants (233 males and 264 females) aged 18-35 years (24.42 ± 5.17) was obtained from Himachal Pradesh state of North India. Both the ears of the participants (994) were examined for anthropometric measurements. A total of 12 anthropometric measurements were taken independently on the left and right ear of each individual with the help of a pair of sliding calipers using a standard method. The sex of the population groups was discriminated against using binary logistic regression and discriminant function analysis. The predictive percentage of sex estimation computed from both the models were substantially the same, that is, 76.3% from DFA and 76.2% from BLR, with nearly comparable (∼0.02) sensitivity, specificity, positive predictive value, and negative predictive values, whereas the values of correct predicted percentage were 0.1% higher in DFA than BLR. Moreover, the other comparison metrics, such as classification error, B-index, and Matthews correlation coefficient indicated that both models performed equally well. The study highlighted that if the assumptions of the statistical methods are met, both methods are equally capable of discriminating the population depending on sex. The study recommends that the discriminant function analysis and binary logistic regression may be used synonymously in forensic research and case-work pertaining to the estimation of sex and various other forensic situations.

A systematic review of photogrammetry as a reliable methodology in gender identification of human skull

One of the most important parameters in the identification process in forensic Medicine and Dentistry is the determination of sex through the skull, based on morphological and metric dimorphism. Photogrammetry is an affordable option that allows the reconstruction of position, orientation, shape, and size, allowing the performance of quantitative and qualitative analyzes to identify the sex of the individual. However, there are few systematic reviews in the literature validating whether photogrammetry is a reliable methodology for sexual identification using human skulls. Therefore, the objective of the current systematic review was to validate whether photogrammetry of dry skulls is reliable as a method for calculating sex in human identification. This revision follows the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and was recorded in the Prospective International Systematic Reviews Registry (PROSPERO) (CRD420223 Systematic Registry) (CRD420223). The inclusion criteria for selecting the studies were based on the PICO question: "Is test photogrammetry reliable as a method for estimating sex in human identification?". A literature search for studies was performed in the databases MEDLINE Scopus, Web of Science, LILACS, and the Cochrane Library. The Kappa agreement presented an approval level of (k = 0.93). This systematic review analyzed 11 ex-vivo studies published between 2001 and 2021. The risk of bias was considered low in 8 of the studies, and high in 3 studies. Based on this systematic review, it can be concluded that the photogrammetry method is viable and reliable in identifying sexual dimorphism.

Evaluation of the (hu)MANid program for sex and ancestry estimation in a diverse, contemporary CT scan-based sample

Human remains from forensic and bioarcheological contexts are often fragmentary, requiring methods for estimating a forensic profile that are based upon limited skeletal features. In 2017, Berg and Keryhercz created an online application, (hu)MANid, that provides sex and ancestry estimation from mandibular morphoscopic traits and linear measurements. In this study, we examine the utility of the (hu)MANid application in a diverse, urban US adult sample (aged 20-45; n = 143) derived from computed tomography (CT) scans. We secondarily conduct a preliminary analysis of the program's utility in a sample of adolescents (aged 15-17; n = 40). Six morphoscopic, and eleven morphometric traits were recorded as directed by the literature associated with the (hu)MANid program. Percent correct classification and posterior predictive values were calculated for the sex and ancestry estimations output by the program; chi-squared tests were employed to compare self-reported and predicted ancestry. In the adult sample, sex was accurately predicted for 75.52% of the sample. Ancestry prediction, however, was less favorable ranging from 19.3% to 50% correct. For the adolescent sample, correct sex estimation (45%) did not surpass what could occur by chance alone, though ancestry prediction fared better than in the larger adult sample (percent correct prediction overall average: 47.5%, range 35.71%-71.43%). The (hu)MANid application shows utility for use with CT scan-derived adult samples for sex estimation, but caution is warranted for ancestry estimation and use with samples that may not have reached full adult maturity.

A Geometric Morphometric Study on Sexual Dimorphism in Viscerocranium

The level of sexual dimorphism manifested by human bones is an important factor for development of effective sex estimation methods. The aim of the study was to investigate the sexual dimorphism in the size and shape of the viscerocranium using geometric morphometric techniques. It also aimed to explore the sex differences in distinct viscerocranial regions and to establish the most dimorphic region with regard to size and shape. Computed tomography images of 156 males and 184 females were used in the study. Three-dimensional coordinates of 31 landmarks were acquired. Five landmark configurations were constructed from the viscerocranium and its orbital, nasal, maxillary, and zygomatic region. Generalized Procrustes superimposition, principal component analysis, and discriminant analysis were applied to each configuration. The significance of the sex differences in size and shape was assessed and significant differences were found in all configurations. The highest accuracy was obtained from both shape and size of the whole viscerocranium. Based on size only, the highest accuracy was achieved by the nasal region. The accuracy based on shape was generally low for all configurations, but the highest result was attained by the orbital region. Hence, size is a better sex discriminator than shape.

Facial soft tissue thickness in forensic facial reconstruction: Impact of regional differences in Brazil

Forensic facial reconstruction aims to assemble and provide the appearance of a face over a skull, in order to lead to recognition of that individual, making possible the application of primary identification methods. The scientific literature presents facial soft tissue thickness (FSTT) tables for reference from a range of different geographic regions. However, the consensus on its importance or on how to use specific population data related to FSTT is not unanimous. Brazil is formed by geographic regions with diverse populations, which are reflected in facial features. This paper aimed to measure and compare FSTT of distinct Brazilian samples to ascertain the need for specific data sets for different regions. A specific protocol for cone beam computed tomography was used to standardize measurement, and it was applied in a sample of 101 subjects. The FSTT measurements of a Brazilian population from the Midwest Region was compared to a previous sample from Southeast, which was collected using the same protocol. High compatibility was observed when comparing the averages of FSTT among samples of these two different geographic regions. Regarding age groups, notable differences on the medium and inferior face were observed in females. Minor variances found are unlikely to affect the practice of forensic facial reconstruction. Facial features, such as eyes, lips, nose, and skin may also be relevant in the differentiation of people from these two areas in Brazil. Therefore, concerning the Southeast and Midwest Brazilian regions, the need to apply different data sets is unnecessary.

Sex estimation with morphometric and morphological characteristics of the crista galli

PURPOSE

The purpose of the present study was to analyze the dimensions of the crista galli (CG) in preserved skulls, and to examine the role of the morphometry and morphology of the CG on the sex estimation.

METHODS

Anteroposterior, superoinferior, and laterolateral diameters of the CG were measured of 207 preserved adult skulls of Anatolia origin with known sex (108 males, 99 females) in the Anatolian population. CG were morphologically classified into three types according to the dimensions. The success of CG dimensions in sex determination was evaluated with ROC analysis, and univariate and multivariate binary logistic regression analysis. The relationship between morphological types of the CG and sex was analyzed with the Chi-square test.

RESULTS

The anteroposterior and superoinferior diameters of the CG significantly were longer in males than females while the laterolateral diameter of the CG was wider in females (p < 0.001). Superoposterior diameter (88.4%) of the CG showed higher sex classification accuracy for sex estimation compared to the laterolateral (82.6%) and anteroposterior diameters (80.6%). When all three parameters were used together, the sex classification accuracy rate was 94.2%. The presence of ossified and tubular types of CG identified the male sex with 85% and 74.6% accuracy rates, respectively while teardrop type CG identified female sex with a 72.2% accuracy rate.

CONCLUSION

CG exhibits sexual dimorphism both morphometrically and morphologically. The height, length, and width measurements and the morphological types of CG can be used in sex determination directly from the skull with high accuracy rates.

Sex determination from lateral cephalometric radiographs using an automated deep learning convolutional neural network

Purpose

Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer’s knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks (CNNs) based on lateral cephalometric radiographs.

Materials and Methods

Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes (male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets.

Results

The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance.

Conclusion

The results confirmed that a CNN could predict a person’s sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

A comprehensive morphometric analysis of crista galli for sex determination with a novel morphological classification on computed tomography images

PURPOSE

This study aimed to examine the morphometry of the crista galli (CG) on paranasal sinus computed tomography (PNCT) images to develop a new approach of morphological classification with objective radiological criteria and to investigate the relationship of morphometric and morphological characteristics with gender.

METHODS

The height, width, and length of the CG were measured on the PNCT images of 533 subjects (266 males, 267 females). Based on the dimensions and the presence of the cavitary component of the CG, the CG was classified into three morphological types. The success of CG dimensions and new morphological classification of CG in the determination of gender was evaluated with ROC and Paired Logistic Regression analyses.

RESULTS

The morphometric cutoff values of the height, width, and length of the CG for the estimation of gender were determined as 15.15, 3.45, and 13.25 mm, respectively. CG length (accuracy 83.7%) showed more successful classification rate on gender determination as compared to height (accuracy: 81.4%), and width (accuracy 81.2%) of the CG. The presence of ossified type CG accurately identified the male sex at a rate of 88.7%, and teardrop type CG determined female sex at a rate of 82.9%. Tubular type CG identified male sex correctly at the rate of 65.8%.

CONCLUSION

The height, length, and width measurements of CG on PNCT images and the new morphological types recommended in this study can be used in the determination of gender with high accuracy rates.

Age-related differences in cranial sexual dimorphism in contemporary Europe

Biomechanical load and hormonal levels tended to change just like the soft and skeletal tissue of the elderly with age. Although aging in both sexes shared common traits, it was assumed that there would be a reduction of sexual dimorphism in aged individuals. The main goals of this study were (1) to evaluate age-related differences in cranial sexual dimorphism during senescence, (2) to determine age-related differences in female and male skulls separately, and (3) to compare skull senescence in Czech and French adult samples as discussed by Musilová et al. (Forensic Sci Int 269:70-77, 2016). The cranial surface was analyzed using coherent point drift-dense correspondence analysis. The study sample consisted of 245 CT scans of heads from recent Czech (83 males and 59 females) and French (52 males and 51 females) individuals. Virtual scans in the age range from 18 to 92 years were analyzed using geometric morphometrics. The cranial form was significantly greater in males in all age categories. After size normalization, sexual dimorphism of the frontal, occipital, and zygomatic regions tended to diminish in the elderly. Its development during aging was caused by morphological changes in both female and male skulls but secular changes must also be taken into account. The most notable aging changes were the widening of the neurocranium and the retrusion of the face, including the forehead, especially after the age of 60 in both sexes. Sexual dimorphism was similar between the Czech and French samples but its age-related differences were partially different because of the population specificity. Cranial senescence was found to degrade the accuracy of sex classification (92-94%) in the range of 2-3%.

Getting Rid of Dichotomous Sex Estimations: Why Logistic Regression Should be Preferred Over Discriminant Function Analysis

Sex estimation is an important part of creating a biological profile for skeletal remains in forensics. The commonly used methods for developing sex estimation equations are discriminant function analysis (DFA) and logistic regression (LogR). LogR equations provide a probability of the predicted sex, while DFA relies on cutoff points to segregate males and females, resulting in a rigid dichotomization of the sexes. This is problematic because sexual dimorphism exists along a continuum and there can be considerable overlap in trait expression between the sexes. In this study, we used humeral measurements to compare the performance of DFA and LogR and found them to be very similar under multiple conditions. The overall cross-validated (leave-one-out) accuracy of DFA (75.76-95.14%) was slightly higher than LogR (75.76-93.82%) for simple and multiple variable equations, and also performed better under varying sample sizes (94.03% vs. 93.78%). Three of five DFA equations outperformed LogR under the B index, while all five LogR equations outperformed the DFA equations under the Q index. Both methods saw an improvement in overall accuracy (DFA: 86.74-95.79%; LogR: 86.74-95.76%) when individuals with a classification probability lower than 0.80 were excluded. Additionally, we propose a method for calculating additional cutoff points (PMarks) based on posterior probability values. In conclusion, we recommend using LogR over DFA due to the increased flexibility, robusticity, and benefits for future users of the statistical models; however, if DFA is preferred, use of the proposed PMarks facilitates future analysis while avoiding unnecessary dichotomization.

Advanced procedures for skull sex estimation using sexually dimorphic morphometric features

This paper introduces an automated method for estimating sex from cranial sex diagnostic traits by extracting and evaluating specialized morphometric features from the glabella, the supraorbital ridge, the occipital protuberance, and the mastoid process. The proposed method was developed and evaluated using two European population samples, a Czech sample comprising 170 crania reconstructed from anonymized CT scans and a Greek sample of 156 crania from the Athens Collection. It is based on a fully automatic algorithm applied on 3D models for extracting sex diagnostic morphometric features which are further processed by computer vision and machine learning algorithms. Classification accuracy was evaluated in a population specific and a population generic 2-way cross-validation scheme. Population-specific accuracy for individual morphometric features ranged from 78.5 to 96.7%, whereas population generic correct classification ranged from 71.7 to 90.8%. Combining all sex diagnostic traits in multi-feature sex estimation yielded correct classification performance in excess of 91% for the entire sample, whereas the sex of about three fourths of the sample could be determined with 100% accuracy according to posterior probability estimates. The proposed method provides an efficient and reliable way to estimate sex from cranial remains, and it offers significant advantages over existing methods. The proposed method can be readily implemented with the skullanalyzer computer program and the estimate_sex.m GNU Octave function, which are freely available under a suitable license.

Pediatric cranial deformations: demographic associations

OBJECTIVE

No study has established a relationship between cranial deformations and demographic factors. While the connection between the Back to Sleep campaign and cranial deformation has been outlined, considerations toward cultural or anthropological differences should also be investigated.

METHODS

The authors conducted a retrospective review of 1499 patients (age range 2 months to less than 19 years) who presented for possible trauma in 2018 and had a negative CT scan. The cranial vault asymmetry index (CVAI) and cranial index (CI) were used to evaluate potential cranial deformations. The cohort was evaluated for differences between sex, race, and ethnicity among 1) all patients and 2) patients within the clinical treatment window (2-24 months of age). Patients categorized as "other" and those for whom data were missing were excluded from analysis.

RESULTS

In the CVAI cohort with available data (n = 1499, although data were missing for each variable), 800 (56.7%) of 1411 patients were male, 1024 (79%) of 1304 patients were Caucasian, 253 (19.4%) of 1304 patients were African American, and 127 (10.3%) of 1236 patients were of Hispanic/Latin American descent. The mean CVAI values were significantly different between sex (p < 0.001) and race (p < 0.001). However, only race was associated with differences in positional posterior plagiocephaly (PPP) diagnosis (p < 0.001). There was no significant difference in CVAI measurements for ethnicity (p = 0.968). Of the 520 patients in the treatment window cohort, 307 (59%) were male. Of the 421 patients with data for race, 334 were Caucasian and 80 were African American; 47 of the 483 patients with ethnicity data were of Hispanic/Latin American descent. There were no differences between mean CVAI values for sex (p = 0.404) or ethnicity (p = 0.600). There were significant differences between the mean CVAI values for Caucasian and African American patients (p < 0.001) and rate of PPP diagnosis (p = 0.02). In the CI cohort with available data (n = 1429, although data were missing for each variable), 849 (56.8%) of 1494 patients were male, 1007 (67.4%) of 1283 were Caucasian, 248 (16.6%) of 1283 were African American, and 138 patients with ethnicity data (n = 1320) of Hispanic/Latin American descent. Within the clinical treatment window cohort with available data, 373 (59.2%) of 630 patients were male, 403 were Caucasian (81.9%), 84 were African American (17.1%), and 55 (10.5%) of 528 patients were of Hispanic/Latin American descent. The mean CI values were not significantly different between sexes (p = 0.450) in either cohort. However, there were significant differences between CI measurements for Caucasian and African American patients (p < 0.001) as well as patients of Hispanic/Latin American descent (p < 0.001) in both cohorts.

CONCLUSIONS

The authors found no significant associations between cranial deformations and sex. However, significant differences exist between Caucasian and African American patients as well as patients with Hispanic/Latin American heritage. These findings suggest cultural or anthropological influences on defining skull deformations. Further investigation into the factors contributing to these differences should be undertaken.

Testing the reliability of 3D-ID software in sex and ancestry estimation with a modern Greek sample

A primary concern in forensic anthropology, when reconstructing the biological profile of an unidentified individual is ancestry and sex estimation. The development of multivariate statistical methods and the assembly of large reference sample databases gave rise to the development of specialized computer software for sex and ancestry estimation. Among various such software, the 3D-ID is the only freely-available program that can handle missing values in the input dataset. The present study evaluates the reliability of 3D-ID in correctly classifying ancestry and sex of 158 test subjects from the Athens Collection, a documented Greek population sample. 3D-ID's classification performance was evaluated both separately and collectively for sex and ancestry. According to our results, the accuracy regarding sex estimation ranged from 74.05% to 86.7% for cases with unknown ancestry and reached 89.87% when testing within the Southeastern European reference group, whereas ancestry estimation accuracy reached 70.9% for correctly classifying the Greek individuals to European population groups. We conclude that 3D-ID software exhibits moderate reliability in ancestry estimation and adequate reliability in sex estimation. The Greek population seems to deviate from the 3D-ID software's reference samples and therefore caution should be taken in interpreting 3D-ID's results of unknown subjects, for which the software's reference sample database may not be representative. 3D-ID's guidelines for using 19-landmark configuration improves the accuracy of ancestry estimation and form variables should be preferred for sexing samples.