Biological Identification of Skulls in Indonesian and Thai Populations: Ancestry Estimation, Sex Determination, Stature Estimation, and Age Estimation / Identificación Biológica de Cráneos en Poblaciones de Indonesia y Tailandia: Estimación de Ascendencia, Determinación del Sexo, Estimación de Estatura y Estimación de Edad

SUMMARY: This review article will present an overview of biological profiles in forensic utilities. The biological profile of the skull in the existing literature can help to identify humans, especially if the condition of the victim found is a result of mutilation or a bomb explosion. When it comes to the precision of identifying skeletal remains, the human skull is frequently cited as being first in the estimation of age and ancestry and second in terms of sex and stature. It can be an alternative to assessing the following biological parameters: sex, age, stature, and ancestry. The implementation of biological profiles in the identification process is very important considering that some cases require the assistance of forensic anthropology. This review article shows the importance of the value of skulls. The method that can be applied is craniometry which can be used to determine sex, age, stature, and estimated ancestry. Different results will occur depending on the completeness of the skull. Therefore, estimation formulas have different accurate results. Discriminant function analysis has been performed on various measurement sets and its discriminant power has been validated by many researchers. Geometric morphometric analysis has become the main tool for shape analysis and many attempts have been made to use it in analyzing skulls. Several methods supported by technology have also been developed. It is hoped that the review article will show significant differences in results between studies in Thailand and Indonesia, even though they are in the same racial group.

Este artículo presenta una descripción general de los perfiles biológicos en las utilidades forenses. El perfil biológico del cráneo en la literatura existente puede ayudar a identificar a los humanos, especialmente si la condición en la que se encuentra la víctima es el resultado de una mutilación o la explosión de una bomba. Cuando se trata de la precisión en la identificación de restos óseos, el cráneo humano se cita con frecuencia como el primero en la estimación de edad y ascendencia y el segundo en términos de sexo y estatura. Puede ser una alternativa para evaluar los siguientes parámetros biológicos: sexo, edad, estatura y ascendencia. La implementación de perfiles biológicos en el proceso de identificación es importante considerando que algunos casos requieren la asistencia de la antropología forense. Este artículo de revisión muestra la importancia del valor de las cnezas óseas. El método que se puede aplicar es la craneometría para determinar el sexo, la edad, la estatura y la ascendencia estimada. Se pueden obtener diferentes resultados dependiendo de la integridad del cráneo. Por lo tanto, las fórmulas de estimación tienen resultados precisos diferentes. Se ha realizado un análisis de función discriminante en varios conjuntos de medidas y muchos investigadores han validado su poder discriminante. El análisis a través de la morfometría geométrica se ha convertido en la principal herramienta para el análisis de formas y se ha utilizado frecuentemente en el análisis de cráneos. También se han desarrollado varios métodos apoyados en la tecnología. Se espera que este trabajo muestre diferencias significativas en los resultados entre los estudios realizados en Tailandia e Indonesia, aunque pertenezcan al mismo grupo racial.

Estimation of ancestry from cranial measurements based on MDCT data acquired in a Japanese and Western Australian population.

The estimation of ancestry is important not only towards establishing identity but also as a required precursor to facilitating the accurate estimation of other attributes such as sex, age at death, and stature. The present study aims to analyze morphological variation in the crania of Japanese and Western Australian individuals and test predictive models based on machine learning for their potential forensic application. The Japanese and Western Australian samples comprise computed tomography (CT) scans of 230 (111 female; 119 male) and 225 adult individuals (112 female; 113 male), respectively. A total of 18 measurements were calculated, and machine learning methods (random forest modeling, RFM; support vector machine, SVM) were used to classify ancestry. The two-way unisex model achieved an overall accuracy of 93.2% for RFM and 97.1% for SVM, respectively. The four-way sex and ancestry model demonstrated an overall classification accuracy of 84.0% for RFM and 93.0% for SVM. The sex-specific models were most accurate in the female samples (♀ 95.1% for RFM and 100% for SVM; ♂91.4% for RFM and 97.4% for SVM). Our findings suggest that cranial measurements acquired in CT images can be used to accurately classify Japanese and Western Australian individuals into their respective population. This is the first study to assess the feasibility of ancestry estimation using three-dimensional CT images of the skull.

The influence of age on greater sciatic notch morphology: testing the Walker method in an Australian population.

Sex estimation is an integral aspect of a forensic biological profile. The pelvis, being the most dimorphic part of the skeleton, has been studied in considerable detail relative to morphological and metric variation. However, empirical data on the effect of age on pelvic morphology relative to sex-specific morphological variation is limited, especially in regard to the estimation of skeletal sex. This study assesses whether there are age-related differences in the distribution of the Walker (2005) morphological scores for the greater sciatic notch (GSN) in an Australian population. Three-dimensional volumetric reconstructions derived from multi-detector computed tomography (MDCT) scans of 567 pelves of 258 females and 309 males aged 18 to 96 years were scored following Walker (2005). Differences in score distributions and means by sex and age group were tested using Pearson's chi-squared test and ANOVA, respectively. The accuracy of sex estimates derived from logistic regression equations was explored using leave-one-out cross-validation. Significant differences were found in score distribution and means among age groups in females, but not in males. There was a tendency toward higher scores in older females. The overall sex estimation accuracy was 87.5%. When comparing age groups 18-49 and 70 + years, estimation accuracy decreased in females (99% vs. 91%), while the opposite was found for males (79% vs. 87%). These findings suggest that age affects GSN morphology. Higher mean scores in older females imply that, on average, the GSN becomes narrower with increasing age. It is thus recommended due consideration of estimated age when assessing sex based on the GSN in unidentified human remains.

Demonstrating the empirical effect of population specificity of anthropological standards in a contemporary Australian population.

The ability to differentiate individuals based on their biological sex is essential for the creation of an accurate anthropological assessment; it is therefore crucial that the standards that facilitate this are likewise accurate. Given the relative paucity of population-specific anthropological standards formulated specifically for application in the contemporary Australian population, forensic anthropological assessments have historically relied on the application of established methods developed using population geographically and/or temporally disparate. The aim of the present paper is, therefore, to assess the accuracy and reliability of established cranial sex estimation methods, developed from geographically distinct populations, as applied to the contemporary Australian population. Comparison between the original stated accuracy and sex bias values (where applicable) and those achieved after application to the Australian population provides insight into the importance of having anthropological standards optimised for application in specific jurisdictions. The sample analysed comprised computed tomographic (CT) cranial scans of 771 (385 female and 386 male) individuals collected from five Australian states/territories. Cranial CT scans were visualised as three-dimensional volume-rendered reconstructions using OsiriX®. On each cranium, 76 cranial landmarks were acquired, and 36 linear inter-landmark measurements were calculated using MorphDB. A total of 35 predictive models taken from Giles and Elliot (1963), Iscan et al. (1995), Ogawa et al. (2013), Steyn and Iscan (1998) and Kranioti et al. (2008) were tested. Application to the Australian population resulted in an average decrease in accuracy of 21.2%, with an associated sex bias range between - 64.0 and 99.7% (average sex bias value of 29.6%), relative to the original studies. The present investigation has highlighted the inherent inaccuracies of applying models derived from geographically and/or temporally disparate populations. It is, therefore, imperative that statistical models developed from a population consistent with the decedent be used for the estimation of sex in forensic casework.

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.

Sex estimation of frontal sinus volume from computed tomography scans in a Western Australian adult population.

With the increasing use of radiological three-dimensional imaging, different metric and morphological aspects of the frontal sinuses (FS) have been examined relative to their potential to aid human identification, including sex estimation. The aim of this study is to assess the validity of the metric analysis of the FS volume for estimating sex in a Western Australian (WA) adult population, following the Michel et al. (2015) method. The right, left and total frontal sinus volume (TFSV) from 99 computed tomography scans (47 males and 52 females) of WA individuals aged 18-40 years were three-dimensionally reconstructed using OsiriX® and their volumes acquired in mm3. Sexual dimorphism in the TFSV was tested, and the accuracy of the Michel et al. (2015) formulae was calculated. Population-specific regression equations were also produced. There was a significant difference between the mean TFSV in males and females. The classification accuracy derived from the Michel et al. (2015) formulae was 59.6%. The WA-specific logistic regression analysis yielded an accuracy of 57.2%. The low accuracy rates preclude the use of the sex estimation method in the WA population, but the volume analysis confirmed the individual variability of the frontal sinuses, which may be useful for personal identification.

Testing the discrepancy between physical and virtual linear measurements.

Virtual calculations of bone morphology are increasingly becoming the golden standard in anthropological sciences, gradually replacing the performance of direct physical measurements. Nevertheless, the potential discrepancy between the two approaches is rarely addressed. Here, we address this question focusing on the second thoracic vertebrae of 24 well-preserved individuals from the skeletal collection of the Forensic Anthropology Unit of Medical School at the National and Kapodistrian University of Athens, Greece. Following traditional osteometric methods, a series of measurements were taken on the vertebral body, both directly (using a digital caliper) as well as on high-resolution 3D surface models. The arithmetic results of the two measuring techniques were then compared through a number of statistical analyses evaluating inter-method precision (Bland-Altman plots, TEM, %TEM and Wilcoxon test). Moreover, the values obtained from each approach were used to develop discriminant function equations for sex determination to evaluate if both approaches provide the same assessment. Both intraobserver and interobserver tests were performed. Although most statistical analyses showed a significant difference between the two measuring techniques, the discriminant function equations for sex determination provided the same assessment. Overall, the results of this experiment support the use of virtual linear measurements, also suggesting that a refinement of digital measuring protocols could improve their level of agreement with traditional direct osteometry.

Comparing morphological (os coxae) and metric (long bone length) sex estimation methods in archaeological collections.

Objectives: The aim of this study is to evaluate the internal consistency of sex estimation using metric (long bone length) and morphological (os coxae) methodologies from different bones and across different archaeological populations from different regions. Materials and Methods: Sex was estimated using characteristics of the pelvis and compared with sex estimations using long bone length. Portuguese (659 females; 906 males) and English (141 females; 277 males) archaeological collections were analysed in this study. A set of long bone length functions were developed using one of the archaeological collections (531 females; 600 males) and its coincidence with sex estimated from the pelvis was compared to the coincidence between the pelvis and long bone length sex estimations using functions developed from contemporary collections. Intra- and inter-observer errors were calculated, as well as the sexual dimorphism index for each bone and osteological collection. Results: The accuracy of the developed functions and the other methods tested is highly variable, ranging between 25 and 100%. The accuracy of the standard forensic methods varied between collections and analysed bones. Discussion: This study reinforces that long bone length is highly population-specific, even between samples of close chronology and geography. Metric methods are good options to strengthen the sex estimations, but they need to be carefully chosen and always report the estimated probability of being male or female in either forensic or archaeological analysis.

Sex estimation based on mandibular measurements.

Medical imaging and machine learning are beneficial approaches in physical and forensic anthropology. They are particularly useful for the development of models for sex identification based on bone remains. The present study uses machine learning algorithms to create models for sex estimation based on mandibular measurements. The sample included head CT scans of 239 adult Bulgarians (116 males and 123 females). Three-dimensional coordinates of 45 landmarks of the mandible were acquired from segmented polygonal models of the skulls of these individuals. Two datasets of mandibular measurements were assembled. The first dataset included 51 measurements: linear, projective, and angular measurements. The second dataset included 990 interlandmark distances. Seven machine learning algorithms (Support Vector Machines, Neural Network, Naïve Bayes, Random Forest, J48, JRip, and Logistic Regression) were applied to the two datasets, and the classification accuracy was evaluated by 10x5-cross-validation. The selection of the best subsets of attributes specific to each of the abovementioned algorithms was done based on the attribute importance evaluated by an attribute selection scheme. In general, the sub-symbolic algorithms achieved higher results than the symbolic ones, except for the logistic regression. The best classification model was learnt by the Support Vector Machines algorithm, which achieved an accuracy of 95.3% on a dataset described by 19 interlandmark distances. In both datasets, the application of advanced attribute selection has led to an increase in the classification accuracy of all algorithms used in the experiments.

Morphometric evaluation of the orbital region for sex determination in a Saudi Arabian population using 3DCT images.

In forensic anthropology, the analysis of variations in the orbitometric features plays a crucial role in identifying the sex of a skull detached from its skeleton. Therefore, it is essential to create population-specific values to maximize the accuracy rate of sex estimation. The objective of this study was to evaluate the orbital region morphometrically for sex determination in a Saudi Arabian population using CT scan images. This study included 456 CT images (228 males and 228 females; age range, 18-58 years). Six orbital measurements and the orbital index of both sides were evaluated to determine sexual dimorphism and laterality using an independent t-test. Sex was estimated using discriminant function analysis. Based on the orbital index, the orbit was classified into megasemes, mesosemes, and microsemes. All the measurements were significantly greater in males than females, but the orbital index showed no significant sexual dimorphism. No significant asymmetry was found in all orbital dimensions. The univariate discriminant function of the orbital measurements showed an accuracy rate between 83.8 and 70%. The multivariate analysis classified the orbital dimensions into male (93%) and female (92.1%) to the highest level of accuracy. In Saudis, the megaseme category was the most prevalent form of orbit. The right orbit height (83.8%), left orbit height (80.7%), right orbit width (79.4%), and left orbit height (79.4%) were the most reliable variables that could be used in sex determination. According to multivariate analysis, the overall accuracy rate for determining sex was 92.1%. While the present study data may not be used alone for sexing a complete skull, there must be various occasions in which this new standard will contribute to determining sex in forensic contexts.

DSP2 for sex determination of miscegenated contemporary hip bones.

The bones of the human pelvis are used in sexual diagnosis generating a high level of accuracy for this type of identification. Morphological and/or morphometric methods are used in the identification of sex. Sexual dimorphism may be affected by ethnic differences in the population. One of the methods for determining sex using hip bone is the 'Diagnose Sexuelle Probabiliste (DSP)' or Probabilistic Sexual Diagnosis (DSP) method. The method presents a new version (Probabilistic Sexual Diagnosis v.2-DSP2) more advisable to be used because it has a more up-to-date database. The objective of this study is to investigate the applicability of the DSP2 method in a population in the Northeast region of Brazil. We used 128 hip bones, 50 female and 78 males, aged between 17 and 101 years, belonging to the Laboratory of Human Identification and Forensic Osteology of the University Federal Government of Pernambuco. The research was conducted between 2019 and 2020 and approved by the Research Ethics Committee of the Federal University of Pernambuco no. 43228015.0.0000.5208. The probability equal to or greater than 0.95 was used as the limit for the determination of sex, and the results were compared with the actual sex of each bone. In the Brazilian collection study, it was observed that the percentage of sex estimation provided by the DSP2 tool using all reference samples was 71.09%, and accuracy was 64.06%. In the analysis of the gender estimate, 82.0% and 78% were obtained for females and males, respectively. Regarding accuracy, it was 64.10% and 55.13% for females and males, respectively. In the contemporary osteological collection of the Northeast region of Brazil, which presents immigrant peoples, we obtained a high index of assertiveness in the DSP2 method. The study concluded that the DSP2 method is important for determining the sex of human skeletons in a miscegenated population.

Sex determination using the clavicle by deep learning in a Thai population.

Determining sex is a critical process in estimating biological profiles from skeletal remains. The clavicle is interesting in studying sex determination because it is durable to the environment, slow to decay, challenging to destroy, making the clavicle useful in autopsies and identification which can then lead to verification. The goal of this study was to use deep learning in determining sex from clavicles within the Thai population and obtain the accuracies for the validation set using a convolutional neural network (GoogLeNet). A total of 200 pairs of clavicles were obtained from 200 Thai persons (100 males and 100 females) as part of a training group. For the deep learning approach, the clavicle was photographed, and each clavicle image was submitted to the training model for sex determination. Training groups of 200 samples were made. Images of the same size were input into the training model. The percentage of the validation set accuracy was calculated from the MATLAB program. GoogLeNet was the best training model and get the result of validation set accuracy. The results of this study found accuracies for a validation set with the highest overall right lateral view of the clavicle with an accuracy of 95%. Accuracy from the validation set of each view of the clavicle can demonstrate the forensic value of sex determination. A deep learning approach with clavicles can determine the sex and is simple to utilize for forensic anthropology professionals.

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.

Sex estimation from the hyoid bone measurements in an adult Eastern Turkish population using 3D CT images, discriminant function analysis, support vector machines, and artificial neural networks☆.

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.

Sex Determination in the Contemporary Chilean Population by Mandible Analysis in Panoramic Radiographies / Estimación Sexual de la Población Chilena Contemporánea Mediante Análisis de la Mandíbula en Radiografías Panorámicas

SUMMARY: Sex identification of a deceased human individual by means of the mandible is very important for forensic dentistry. The aim of the present study was to determine the sex of Chilean individuals by mandible analysis in panoramic radiographies. Linear and angular parameters of the mandible were analyzed from panoramic radiographies (PR). The study included PR of adult Chilean individuals, of both sexes, with optimum solution and contrast, and which allowed the angles and rami of the mandible to be viewed. Sex was determined by univariate and bivariate discriminant function analysis. The sample consisted of 594 PR of individuals aged between 18 and 84 years. The best sex predictor using univariate discriminant function analysis was the mandibular ramus height (MRH) (74.1 %), followed by the distance from the mental foramen - mandibular base (DMF-MB) (69.1 %) and the bicondylar breadth (BC) (66.7 %). The parameters that presented the lowest sex prediction were the angle of the mandible (AM) with 55.0 % and the distance between mental foramina (DMF) with 53.7 %. The best sex prediction was obtained by the step model of discriminant function analysis (80.2 %), including only three parameters: MRH, BC and DMF-MB. The parameters height of the mandibular ramus, bicondylar breadth and distance from the mental foramen - base of the mandible are good predictors of sex in Chilean individuals when used in conjunction; they are therefore indicated for sex determination in the contemporary Chilean population.

La identificación humana de un individuo fallecido a través de la mandíbula es muy relevante para la odontología forense. El objetivo de este estudio fue estimar el sexo de individuos Chilenos a través del análisis de la mandíbula, utilizando radiografías panorámicas. Fueron analizados parámetros lineales y angulares de la mandíbula, a través de radiografías panorámicas (RP). Se incluyeron RP de individuos chilenos adultos, ambos sexos, con solución y contraste óptimos, y que permitían la visualización de los ángulos y ramas de la mandíbula. Se realizó análisis por función discriminante univariada y bivariada para estimación del sexo. Fueron incluidas 594 RP de individuos entre 18 y 84 años. Para el análisis de función discriminante univariado, la altura de la rama mandibular (ARM) fue el parámetro más predictivo (74,1 %), seguido de la distancia foramen mentoniano - base de la mandíbula (DFM-BM) (69,1 %) y el ancho bicondilar (ABCo) (66,7 %). Los parámetros que presentaron menor predicción sexual fueron el ángulo de la mandíbula (AM) con un 55,0 % y la distancia inter-forámenes mentonianos (DIFM), con el 53.7 %. El análisis por pasos fue el modelo de análisis de función discriminante que presentó la mayor predicción sexual (79,5 %), en el cual fueron incluidos sólo tres parámetros: ARM, ABCo y DFM-BM. Los parámetros altura de la rama de la mandíbula, ancho bicondilar y distancia desde el foramen mentoniano hasta la base de la mandíbula son buenos predictores del sexo en individuos Chilenos cuando utilizados en conjunto y están indicados para estimar el sexo en la población chilena contemporánea.

Artificial intelligence-based forensic sex determination of East Asian cadavers from skull morphology.

Identification of unknown cadavers is an important task for forensic scientists. Forensic scientists attempt to identify skeletal remains based on factors including age, sex, and dental treatment remains. Forensic scientists commonly consider skull or pelvic shape to evaluate the sex; however, these evaluations require sufficient experience and knowledge and lack objectivity and reproducibility. To ensure objectivity and reproducibility for sex evaluation, we applied a gated attention-based multiple-instance learning model to three-dimensional (3D) skull images reconstructed from postmortem head computed tomography scans. We preprocessed the images, trained with 864 training data, validated the model with 124 validation data, and evaluated the performance of our model in terms of accuracy with 246 test data. Furthermore, three forensic scientists evaluated the 3D skull images, and their performances were compared with those of the model. Our model showed an accuracy of 0.93, which was higher than that of the forensic scientists. Our model primarily focused on the entire skull owing to visualization but focused less on the areas often investigated by forensic scientists. In summary, our model may serve as a supportive tool to identify cadaver sex based on skull shape. Further studies are required to improve the model's performance.

How reliable is the application of the sex classifier based on exocranial surface (Musilová et al., 2016) for geographically and temporally distant skull series.

Sex estimation is one of the crucial trends in cases of findings of unknown skeletal remains in forensics and bioarchaeology. The changing nature of sexual dimorphism (population specificity, secular trend, other external and internal factors influence) brings challenges to developing new methods; and there are new aims to be independent of these changes such, as the method by Musilová et al. (2016). These methods need to be evaluated on different datasets to determine if they are truly reliable among populations from different places and times, in the case of bioarchaeology. This study assessed the application of the aforementioned method on non-European contemporary and ancient populations to identify the reliability of the method on this separate dataset. The study sample consisted of 96 CT scans of skulls from contemporary Egyptians and 54 3D models of skulls from the Egyptian Old Kingdom Period (2700-2180 BC). The classifier method, previously tested on both Czech and French populations, yielded high accuracies (over 90 %) for sex estimation. For the contemporary Egyptian skull sample, the classifier was able to determine males versus females with an 89.59 % accuracy rate and an AUC value (area under the curve - a measure of the combined specificity and sensitivity of the test) of 0.99; this proves that the classifier is reliable even with a lower degree of accuracy. Conversely, the Old Kingdom Period sample yielded a lower level of accuracy at around 70 % (61.11 %, precisely), although with an AUC value of 0.92, the result is not considered reliable.

Sex determination using mandibular ramus flexure in South Indian population - A retrospective study.

Sex determination or sex estimation from a single or fragment of bone is always difficult in the absence of other bones from the same individual. The current study was an attempt to estimate the sex of an individual from the posterior ramus of mandible or the mandibular ramus flexure. A retrospective study was conducted using orthopantomographs (OPGs) of 200 males and 200 females between the age group of 20 - 70 years. Each radiographic image was examined for the presence of a flexure or notching on the posterior border of the ramus in relation to occlusal plane as the method followed by Loth & Henneberg 1996.The study resulted in samples that were correctly classified as females 59.5% and males 57.5 %. The overall correct sex estimation was achieved in 58.5% of the cases. The predictive accuracy or assessment was higher for females compared to males. Consequently, the posterior ramus of mandible or mandibular ramus flexure can be considered as supplementary rather than a definitive means of sex determination. Hence, it is preferable to include as many parameters as possible to attain optimal accuracy.

Application of Sphenoid Sinus Morphological Characteristics in Sex Estimation Based on Magnetic Resonance Imaging Images / Aplicación de las Características Morfológicas del Seno Esfenoidal en la Estimación del Sexo Basada en Imágenes de Resonancia Magnética

SUMMARY: Sex determination of unknown persons plays an important role in forensic science. As most bones used for sex determination are recovered in incomplete state, it is often necessary to use bones that are recovered intact e.g., the sphenoid sinus. This study aimed to evaluate the diagnostic value of sphenoid sinuses dimensions for sex determination using Magnetic Resonance Imaging (MRI) images in Chinese adults. MRI images of 79 sphenoid sinuses (from 44 men and 35 women) were retrospectively selected. The height, anterior-posterior diameter, area, and perimeter were measured in the midsagittal view of the sphenoid sinuses. All data were subjected to descriptive and discriminative functional analysis with unpaired t-test and canonical discriminant. Comparison between male and female groups showed significant statistical differences regarding the height, anterior-posterior diameter, area, and perimeter of sphenoid sinuses. The predictive accuracy rate of the sphenoid sinus to identify sex was 63.6 % in males and 62.9 % in females with an overall accuracy of 63.3 %. This study proposed the importance of sexual dimorphism of sphenoid sinus dimensions, especially if other methods are not available. It suggested using MRI in forensics science thus obviating the complete dependence on the usage of conventional computed tomography (CT) and facilitating the study of forensic anatomy at the level of soft tissue.

La determinación del sexo de personas desconocidas juega un papel importante en la ciencia forense. Como la mayoría de los huesos utilizados para la determinación del sexo se recuperan en un estado incompleto, a menudo es necesario utilizar huesos recuperados intactos, por ejemplo, el seno esfenoidal. Este estudio tuvo como objetivo evaluar el valor diagnóstico de las dimensiones de los senos esfenoidales para la determinación del sexo utilizando imágenes de resonancia magnética en individuos adultos chinos. Se seleccionaron retrospectivamente imágenes de resonancia magnética de 79 senos esfenoidales (de 44 hombres y 35 mujeres). La altura, el diámetro anteroposterior, el área y el perímetro de los senos esfenoidales, se midieron en vista mediana sagital. Todos los datos se sometieron a análisis funcional descriptivo y discriminativo con prueba t no pareada y discriminante canónico. La comparación entre los grupos de hombres y mujeres mostró diferencias estadísticas significativas en cuanto a la altura, el diámetro anteroposterior, el área y el perímetro de los senos esfenoidales. La tasa de precisión predictiva del seno esfenoidal para identificar el sexo fue del 63,6 % en hombres y del 62,9 % en mujeres, con una precisión general del 63,3 %. Este estudio propuso la importancia del dimorfismo sexual de las dimensiones del seno esfenoidal, especialmente si no se dispone de otros métodos. Se sugiere utilizar la resonancia magnética en la ciencia forense, obviando así la dependencia total del uso de la tomografía computarizada convencional y facilitando con esto el estudio de la anatomía forense a nivel de los tejidos blandos.

Sex Determination by the Machine Learning Algorithms Through Using Morphometric Measurements of the Carpal, Metacarpal, and Phalangeal Bones / Determinación del Sexo Mediante Algoritmos de Aprendizaje Automático Mediante el Uso de Mediciones Morfométricas de los Huesos del Carpo, el Metacarpo y las Falanges

SUMMARY: In the study, it was aimed to predict sex from hand measurements using machine learning algorithms (MLA). Measurements were made on MR images of 60 men and 60 women. Determined parameters; hand length (HL), palm length (PL), hand width (HW), wrist width (EBG), metacarpal I length (MIL), metacarpal I width (MIW), metacarpal II length (MIIL), metacarpal II width (MIIW), metacarpal III length (MIIL), metacarpal III width (MIIIW), metacarpal IV length (MIVL), metacarpal IV width (MIVW), metacarpal V length (MVL), metacarpal V width (MVW), phalanx I length (PILL), measured as phalanx II length (PIIL), phalanx III length (PIIL), phalanx IV length (PIVL), phalanx V length (PVL). In addition, the hand index (HI) was calculated. Logistic Regression (LR), Random Forest (RF), Linear Discriminant Analysis (LDA), K-nearest neighbour (KNN) and Naive Bayes (NB) were used as MLAs. In the study, the KNN algorithm's Accuracy, SEN, F1 and Specificity ratios were determined as 88 %. In this study using MLA, it is understood that the highest accuracy belongs to the KNN algorithm. Except for the hand's MIIW, MIIIW, MIVW, MVW, HI variables, other variables were statistically significant in terms of sex difference.

En el estudio, el objetivo era predecir el sexo a partir de mediciones manuales utilizando algoritmos de aprendizaje automático (MLA). Las mediciones se realizaron en imágenes de RM de 60 hombres y 60 mujeres. Parámetros determinados; longitud de la mano (HL), longitud de la palma (PL), ancho de la mano (HW), ancho de la muñeca (EBG), longitud del metacarpiano I (MIL), ancho del metacarpiano I (MIW), longitud del metacarpiano II (MIIL), ancho del metacarpiano II (MIIW), longitud del metacarpiano III (MIIL), ancho del metacarpiano III (MIIIW), longitud del metacarpiano IV (MIVL), ancho del metacarpiano IV (MIVW), longitud del metacarpiano V (MVL), ancho del metacarpiano V (MVW), longitud de la falange I (PILL), medido como longitud de la falange II (PIIL), longitud de la falange III (PIIL), longitud de la falange IV (PIVL), longitud de la falange V (PVL). Además, se calculó el índice de la mano (HI). Regresión logística (LR), Random Forest (RF), Análisis discriminante lineal (LDA), K-vecino más cercano (KNN) y Naive Bayes (NB) se utilizaron como MLA. En el estudio, las proporciones de precisión, SEN, F1 y especificidad del algoritmo KNN se determinaron en un 88 %. En este estudio que utiliza MLA, se entiende que la mayor precisión pertenece al algoritmo KNN. Excepto por las variables MIIW, MIIIW, MIVW, MVW, HI de la mano, otras variables fueron estadísticamente significativas en términos de diferencia de sexo.