Sex estimation by CT image analysis of the rib cage in a Mediterranean population.

Sexual dimorphism in the human species is key to the development of sex estimation techniques in the human skeleton. This dimorphism is manifested, as in other regions of the skeleton, in the bones that constitute the thoracic cage, according to the existing bibliography. In this aspect, the study of the human skeleton through 3D images has also proved to be useful for the development and validation of sex estimation methodologies for the reconstruction of the osteobiological profile.For this purpose, a sample of 240 thoracic CT scans of adult individuals was selected from a collection of 3D images belonging to the University of Granada, provided by the Castilla-La Mancha Health Service (SESCAM). Different measurements of the thoracic bones (ribs R2 to R5 width, sternum length and width, and clavicles width) have been taken with OsiriX software, with the aim of developing discriminant functions for sex estimation.The obtained results are positive, allowing sex estimation through 3D images of the thorax with up to 89.6% accuracy through discriminant functions, which shows the usefulness of image analysis for the reconstruction of the osteobiological profile.

Sex estimation from patellar measurements in a contemporary Italian population: a machine learning approach.

Biological sex estimation in forensic anthropology is a crucial topic, and the patella has shown promise in this regard due to its sexual dimorphism. This study uses 12 machine learning models for sex estimation based on three patellar measurements (maximum height, breadth, and thickness). Data was collected from 180 skeletons of a contemporary Italian population (83 males and 97 females) as well as from an independent sample of 21 forensic cases (13 males and 8 females). Statistical analyses indicated that each of the variables exhibited significant sexual dimorphism. To predict biological sex, the classifiers were built using 70% of a reference sample, then tested on the remaining 30% of the original sample and then tested again on the independent sample. The different classifiers generated accuracies varied between 0.85 and 0.91 on the reference sample and between 0.71 and 0.95 for the validation sample. SVM classifier stood out with the highest accuracy and seemed the best model for our study.This study contributes to the growing application of machine learning in forensic anthropology by being the first to apply such techniques to patellar measurements in an Italian population. It aims to enhance the accuracy and efficiency of biological sex estimation from the patella, building on promising results observed with other skeletal elements.

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.

A statistical evaluation of the sexual dimorphism of the acetabulum in an Iberian population.

Sex estimation is essential for human identification within bioarchaeological and medico-legal contexts. Amongst the sexually dimorphic skeletal elements commonly utilised for this purpose, the pelvis is usually preferred because of its direct relationship with reproduction. Furthermore, the posterior part of the innominate bone has proven to have better preservation within degraded contexts. With the aim of investigating the potential of the vertical acetabular diameter as a sex marker, 668 documented individuals from three different Iberian skeletal collections were randomly divided into training and test samples and eventually analysed using different statistical approaches. Two traditional (Discriminant Function Analysis and Logistic Regression Analysis) and four Machine learning methodologies (Support Vector Classification, Decision Tree Classification, k Nearest Neighbour Classification, and Neural Networks) were performed and compared. Amongst these statistical modalities, Machine Learning methodologies yielded better accuracy outcomes, with DTC garnering highest accuracy percentages of 83.59% and 89.85% with the sex-pooled and female samples, respectively. With males, ANN yielded highest accuracy percentage of 87.70%, when compared to other statistical approaches. Higher accuracy obtained with ML, along with its minimal statistical assumptions, warrant these approaches to be increasingly utilised for further investigations involving sex estimation and human identification. In this line, the creation of a statistical platform with easier user interface can render such robust statistical modalities accessible to researchers and practitioners, effectively maximising its practical use. Future investigations should attempt to achieve this goal, alongside examining the influence of factors such as age, on the obtained accuracy outcomes.

Nonmetric sex estimation in a contemporary Indonesian population: a validation study using clinical pelvic MSCT scans.

Klales et al. (2012) is a popular standard for the estimation of skeletal sex. Since its publication, a number of studies have demonstrated that population-specific applications of Klales improve classification accuracy. However, it has also been shown that age appears to affect the expression of dimorphism in the pelvis across the lifespan. As such, the present study examines the accuracy of Klales, and the modified global standard of Kenyhercz et al. (2017), in a contemporary Indonesian population, including quantifying the effect of age. Pelvic multi-slice CT scans of 378 individuals (213 female; 165 male) were analysed in OsiriX®. Both standards were tested and Indonesian-specific models thereafter derived.When applied to the Indonesian sample, both the Klales and Kenyhercz standards resulted in lower classification accuracy relative to the original studies. In considering the Indonesian-specific models, the ventral arc was the most accurate for the classification of sex, at 93.3% with a - 3.0% sex bias. The accuracy of the three-trait model was 94.4%, with a - 5.5% sex bias. Age was shown to significantly affect the distribution of pelvic trait scores. As such, age-dependent models were also derived, with the standard for individuals between 30 and 49 years the most accurate, at 93.1% and a sex bias of - 4.0%. Accuracy was lower in individuals aged ≥ 50 years, at 91.3% and a sex bias of 4.1%. These findings support the importance of establishing population-specific standards and to facilitate improved accuracy and capabilities for forensic practitioners in Indonesia.

Automatic variable extraction from 3D coxal bone models for sex estimation using the DSP2 method.

Thanks to technical progress and the availability of virtual data, sex estimation methods as part of a biological profile are undergoing an inevitable evolution. Further reductions in subjectivity, but potentially also in measurement errors, can be brought by approaches that automate the extraction of variables. Such automatization also significantly accelerates and facilitates the specialist's work. The aim of this study is (1) to apply a previously proposed algorithm (Kuchar et al. 2021) to automatically extract 10 variables used for the DSP2 sex estimation method, and (2) to test the robustness of the new automatic approach in a current heterogeneous population. For the first aim, we used a sample of 240 3D scans of pelvic bones from the same individuals, which were measured manually for the DSP database. For the second aim a sample of 108 pelvic bones from the New Mexico Decedent Image Database was used. The results showed high agreement between automatic and manual measurements with rTEM below 5% for all dimensions except two. The accuracy of final sex estimates based on all 10 variables was excellent (error rate 0.3%). However, we observed a higher number of undetermined individuals in the Portuguese sample (25% of males) and the New Mexican sample (36.5% of females). In conclusion, the procedure for automatic dimension extraction was successfully applied both to a different type of data and to a heterogeneous population.

A morphoscopic exploration of cranial sexual dimorphism among modern South Africans using computed tomography scans.

Continual re-evaluation of standards for forensic anthropological analyses are necessary, particularly as new methods are explored or as populations change. Indian South Africans are not a new addition to the South African population; however, a paucity of skeletal material is available for analysis from medical school collections, which has resulted in a lack of information on the sexual dimorphism in the crania. For comparable data, computed tomography scans of modern Black, Coloured and White South Africans were included in addition to Indian South Africans. Four cranial morphoscopic traits, were assessed on 408 modern South Africans (equal sex and population distribution). Frequencies, Chi-squared tests, binary logistic regression and random forest modelling were used to assess the data. Males were more robust than females for all populations, while White South African males were the most robust, and Black South African females were the most gracile. Population differences were noted among most groups for at least two variables, necessitating the creation of populations-specific binary logistic regression equations. Only White and Coloured South Africans were not significantly different. Indian South Africans obtained the highest correct classifications for binary logistic regression (94.1%) and random forest modelling (95.7%) and Coloured South Africans had the lowest correct classifications (88.8% and 88.0%, respectively). This study provides a description of the patterns of sexual dimorphism in four cranial morphoscopic traits in the current South African population, as well as binary logistic regression functions for sex estimation of Black, Coloured, Indian and White South Africans.

Different machine learning methods based on maxillary sinus in sex estimation for northwestern Chinese Han population.

BACKGROUND & OBJECTIVE: Sex estimation is a critical aspect of forensic expertise. Some special anatomical structures, such as the maxillary sinus, can still maintain integrity in harsh environmental conditions and may be served as a basis for sex estimation. Due to the complex nature of sex estimation, several studies have been conducted using different machine learning algorithms to improve the accuracy of sex prediction from anatomical measurements. MATERIAL & METHODS: In this study, linear data of the maxillary sinus in the population of northwest China by using Cone-Beam Computed Tomography (CBCT) were collected and utilized to develop logistic, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and random forest (RF) models for sex estimation with R 4.3.1. CBCT images from 477 samples of Han population (75 males and 81 females, aged 5-17 years; 162 males and 159 females, aged 18-72) were used to establish and verify the model. Length (MSL), width (MSW), height (MSH) of both the left and right maxillary sinuses and distance of lateral wall between two maxillary sinuses (distance) were measured. 80% of the data were randomly picked as the training set and others were testing set. Besides, these samples were grouped by age bracket and fitted models as an attempt. RESULTS: Overall, the accuracy of the sex estimation for individuals over 18 years old on the testing set was 77.78%, with a slightly higher accuracy rate for males at 78.12% compared to females at 77.42%. However, accuracy of sex estimation for individuals under 18 was challenging. In comparison to logistic, KNN and SVM, RF exhibited higher accuracy rates. Moreover, incorporating age as a variable improved the accuracy of sex estimation, particularly in the 18-27 age group, where the accuracy rate increased to 88.46%. Meanwhile, all variables showed a linear correlation with age. CONCLUSION: The linear measurements of the maxillary sinus could be a valuable tool for sex estimation in individuals aged 18 and over. A robust RF model has been developed for sex estimation within the Han population residing in the northwestern region of China. The accuracy of sex estimation could be higher when age is used as a predictive variable.

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.

Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population.

Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.

Validating the use of clinical MSCT scans for cranial nonmetric sex estimation in a contemporary Indonesian population.

There is renewed interest in Asia for the development of forensic anthropological standards, partly due to the need for population-specific models to maintain high classification accuracies. At present, there are no known studies utilising morphoscopic standards specific to the Indonesian population. Craniometric analyses can often be time-consuming; morphoscopic assessments are quicker and are also known to be reliable and accurate. One of the most utilised morphoscopic standards for the estimation of skeletal sex is that of Walker (2008). Its application across population groups demonstrated reduced accuracies outside of the United States; population-specific predictive models would thus serve to improve the identification process of unknown skeletal remains. Digital imaging also allows for the validation of standards on a contemporary population and is an appropriate proxy to physical skeletal material.The present study quantifies the applicability of the Walker standard to a contemporary Indonesian population. A total of 200 cranial MSCT scans from a hospital in Makassar were analysed using OsiriX®. Scoring was performed in accordance with the Walker standard. Five univariate and nine multivariate predictive models were derived using single trait and multi-trait combinations. The best performing univariate model included the glabella, with a total classification accuracy of 82.0% and a sex bias of 14.6%. Classification accuracy with all traits considered was at 95.2% for females and 82.8% for males with a sex bias of 12.5%. These results provide forensic practitioners in Indonesia with an appropriate morphoscopic sex estimation standard, strengthening their capabilities in the field and improving judicial outcomes.

Validation and recalibration of sex estimation methods using pubic nonmetric traits for the Chilean population.

Chile had a violent military coup (1973-1990) that resulted in 3,000 victims declared detained, missing or killed; many are still missing and unidentified. Currently, the Human Rights Unit of the Forensic Medical Service in Chile applies globally recognised forensic anthropological approaches, but many of these methods have not been validated in a Chilean sample. As current research has demonstrated population-specificity with extant methods, the present study aims to validate sex estimation methods in a Chilean population and thereafter establish population-specific equations. A sample of 265 os coxae of known age and sex of adult Chileans from the Santiago Subactual Osteology Collection were analysed. Visual assessment and scoring of the pelvic traits were performed in accordance with the Phenice (1969) and Klales et al. (2012) methods. The accuracy of Phenice (1969) in the Chilean sample was 96.98%, with a sex bias of 7.68%. Klales et al. (2012) achieved 87.17% accuracy with a sex bias of -15.39%. Although both methods showed acceptable classification accuracy, the associated sex bias values are unacceptable in forensic practice. Therefore, six univariate and eight multivariate predictive models were formulated for the Chilean population. The most accurate univariate model was the ventral arc at 96.6%, with a sex bias of 5.2%. Classification accuracy using all traits was 97.0%, with a sex bias of 7.7%. This study provides Chilean practitioners a population-specific morphoscopic standard with associated classification probabilities acceptable to accomplish legal admissibility requirements in human rights and criminal cases specific to the second half of the 20th century.

What we see is what we touch? Sex estimation on the skull in virtual anthropology.

BACKGROUND: The increased use of virtual bone images in forensic anthropology requires a comprehensive study on the observational errors between dry bones and CT reconstructions. Here, we focus on the consistency of nonmetric sex estimation traits on the human skull. MATERIALS AND METHODS: We scored nine nonmetric traits on dry crania and mandibles (n = 223) of archaeological origin and their CT reconstructions. Additionally, we 3D surface scanned a subsample (n = 50) and repeated our observations. Due to the intricate anatomy of the mental eminence, we split it into two separate traits: the bilateral mental tubercles and the midsagittal mental protuberance. We provide illustrations and descriptions for both these traits. RESULTS: We obtained supreme consistency values between the CT and 3D surface modalities. The most consistent cranial traits were the glabella and the supraorbital margin, followed by the nuchal crest, zygomatic extension, mental tubercles, mental protuberance, mental eminence, mastoid process and ramus flexure, in descending order. The mental tubercles show higher consistency scores than the mental eminence and the mental protuberance. DISCUSSION: The increased interchangeability of the virtual modalities with each other as compared to the dry bone modality could be due to the lack of tactility on both the CT and surface scans. Moreover, tactility appears less essential with experience than a precise trait description. Future studies could revolve around the most consistent cranial traits, combining them with pelvic traits from a previous study, to test for accuracy.

Sex estimation from skull measurements of a contemporary Japanese population using three-dimensional computed tomography images.

In this study, we assessed the sexual dimorphism of the contemporary Japanese skull and established sex discriminant function equations based on cranial measurements using three-dimensional (3D) computed tomography (CT) images. The CT images of 263 corpses (142 males, 121 females) that underwent postmortem CT scanning and subsequent forensic autopsy were evaluated. Twenty-one cranial measurements were obtained from 3D CT reconstructed images, which extracted only bone data. We performed descriptive statistics and discriminant function analyses for the measurements. Nineteen measurements were significantly larger in males, suggesting sexual dimorphism of the Japanese skulls. Univariate discriminant function analyses using these measurements showed a sex classification accuracy of 57.8-88.2%, and bizygomatic breadth provided the highest correct prediction rate. Multivariate discriminant function analyses offered the most accurate model using seven variables with an estimation rate of 93.9%. Our results suggest that cranial measurements based on 3D CT images may help in the sex estimation of unidentified bodies in a contemporary Japanese population.

Classification performance of the Sella-Tunis et al. (2017) sex estimation method in Czech population: different posterior probability threshold approaches.

In this study we tested classification performance of a sex estimation method from the mandible originally developed by Sella-Tunis et al. (2017) on a heterogeneous Israeli population. Mandibular linear dimensions were measured on 60 CT scans derived from the Czech living population. Classification performance of Israeli discriminant functions (DFs-IL) was analyzed in comparison with calculated Czech discriminant functions (DFs-CZ) while different posterior probability thresholds (currently discussed in the forensic literature) were employed. Our results comprehensively illustrate sensitivity of different discriminant functions to population differences in body size and degree of sexual dimorphism. We demonstrate that the error rate may be biased when presented per posterior probability threshold. DF-IL 1 showed least sensitivity to population origin and fulfilled criteria of sufficient classification performance when applied on the Czech sample with a minimum posterior probability threshold of 0.88 reaching overall accuracy ≥ 95%, zero sex bias, and 80% of classified individuals. The last parameter was higher in DF-CZ 1 which was the main difference between those two DFs suggesting relatively low dependance on population origin. As the use of population-specific methods is often prevented by complicated assessment of population origin, DF-IL 1 is a candidate for a sufficiently robust method that could be reliably applied outside the reference sample, and thus, its classification performance deserves further testing on more population samples.

No Bones About It: Sex Is Binary.

Anthropologists have led the way in formulating techniques that reveal skeletal differences between males and females. Understanding of physical differences in the pelvis related to childbirth, hormonal impacts on bones, and extensive comparative studies have provided anthropologists with an array of traits and measurements that help them estimate sex using just bones. Forensic anthropologists and bioarcheologists are improving their ability to differentiate males and females by increasing research on a variety of postcranial bones and through the use of molecular data, especially new methods called proteomics, to identify sex in prepubescent juveniles. As remains from more cultures and time periods are studied, sex identification will continue to improve, because skeletal sex differences are in large part biologically determined. Yet, anthropologists have also been at the forefront of arguing that sex lies on a spectrum. Anthropologists who view sex as on a spectrum may deter sex identification progress; from their perspective, an individual of an undetermined sex may just be a nonbinary individual. Anthropologists who consider sex is on a spectrum are coming to this conclusion in part because they are looking for anatomical ideals, mistaking pathology for variation, and confusing independent variables with dependent variables. Nonetheless, anthropologists need to continue to improve sex identification techniques to reconstruct the past accurately, which may reveal less strict sex roles than previously presumed and help with the identification of crime victims. Forensic anthropologists should also increase their efforts to identify whether individuals have undergone medical procedures intended to change one's gender due to the current rise in transitioning individuals.

Sex estimation using maxillary sinus volume for Chinese subjects based on cone-beam computed tomography.

BACKGROUND: Sex estimate is a key stage in forensic science for identifying individuals. Some anatomical structures may be useful for sex estimation since they retain their integrity even after highly severe events. However, few studies are focusing on the Chinese population. Some researchers used teeth for sex estimation, but comparison with maxillary sinus were lack. As a result, the objective of this research is to develop a sex estimation formula for the northwestern Chinese population by the volume of the maxillary sinus and compare with the accuracy of sex estimation based on teeth through cone-beam computed tomography (CBCT). METHODS: CBCT images from 349 samples were used to establish and verify the formula. The volume of both the left and right maxillary sinuses was measured and examined for appropriate formula coefficients. To create the formula, we randomly picked 80% of the data as the training set and 20% of the samples as the testing set. Another set of samples, including 20 males and 20 females, were used to compare the accuracy of maxillary sinuses and teeth. RESULTS: Overall, sex estimation accuracy by volume of the left maxillary sinus can reach 78.57%, while by the volume of the right maxillary sinus can reach 74.29%. The accuracy for females, which can reach 91.43% using the left maxillary sinus, was significantly higher than that for males, which was 65.71%. The result also shows that maxillary sinus volume was higher in males. The comparison with the available results using measurements of teeth for sex estimation performed by our group showed that the accuracy of sex estimation using canines volume was higher than the one using maxillary sinus volume, the accuracies based on mesiodistal diameter of canine and first molar were the same or lower than the volume of maxillary sinus. CONCLUSIONS: The study demonstrates that measurement of maxillary sinus volume based on CBCT scans was an available and alternative method for sex estimation. And we established a method to accurately assess the sex of the northwest Chinese population. The comparison with the results of teeth measurements made the conclusion more reliable.

The utility of sternum in creating a biological profile: A review and future directions.

This review delves into the forensic utility of the sternum in creating a biological profile, focusing on sex, stature, and age estimation. Emphasizing the sternum's significance in challenging scenarios, the study supports the combined length of the manubrium and sternal body as a crucial indicator in sex and stature estimation. However, it highlights the need for caution in applying findings across diverse populations and questions the reliability of Hyrtl's law. Age estimation, primarily based on morphological changes and ossification ages, is explored, with one study showing promise but requiring further validation. While acknowledging the sternum's advantages, the review underscores potential limitations and the absence of specific studies on ancestry estimation, leaving this aspect open for future research. In conclusion, the review provides a comprehensive overview of the sternum's forensic applications, urging continued research to enhance accuracy and applicability.

Validation of discriminant functions from the rib necks in two Portuguese adult identified populations.

Sexual dimorphism is the basis for sex estimation research in skeletal remains, helping the positive identification of individuals in forensic backgrounds. In this regard, it has been proved that the metrical analysis of the costal neck of the first four ribs in Spanish contemporary adult skeletons shows high sexual dimorphism, especially in the first rib. However, the validation of any method developed in identified skeletal collections must be compulsorily reproduced in other skeletonized sets to verify either its potential application in individuals from other locations, or the existence of possible biases associated with inter-population variation. Due to the geographical and socioeconomic proximity between Spain and Portugal, this paper aims to check the utility of the discriminant functions designed by Partido-Navadijo et al. (2021) in the two skeletal collections of the University of Coimbra (Portugal). Results show the utility of these discriminant functions in Portuguese population, with frequencies of cases correctly assigned reaching up to 93.6% in the Coimbra Identified Skeletal Collection (CISC); and up to 92.6% in the 21st Century Identified Skeletal Collection (CEI/XXI). However, two particularities between the Spanish and Portuguese osteological assemblages can be highlighted: the possible existence of secular changes in relation to the CISC collection (1904-1938); and a reversal in the degree of costal dimorphism, being the third and fourth ribs of the CEI/XXI collection more dimorphic than the first ones.

Semi-supervised automatic dental age and sex estimation using a hybrid transformer model.

Teeth-based age and sex estimation is an important task in mass disasters, criminal scenes, and archeology. Although various methods have been proposed, most of them are subjective and influenced by observers' experiences. In this study, we aimed to develop a deep learning model for automatic dental age and sex estimation from orthopantomograms (OPGs) and compare to manual methods. A large dataset of 15,195 OPGs (age range, 16 ~ 50 years; mean age, 29.65 years ± 9.36 [SD]; 10,218 females) was used to train and test a hybrid deep learning model which is a combination of convolutional neural network and transformer model. The final performance of this model was evaluated on additional independent 100 OPGs and compared to the manual method for external validation. In the test of 1413 OPGs, the mean absolute error (MAE) of age estimation was 2.61 years by this model. The accuracy and the area under the receiver operating characteristic curve (AUC) of sex estimation were 95.54% and 0.984. The heatmap indicated that the crown and pulp chamber of premolars and molars contain the most age-related information. In the additional independent 100 OPGs, this model achieved an MAE of 3.28 years for males and 3.79 years for females. The accuracy of this model was much higher than that of the manual models. Therefore, this model has the potential to assist radiologists in automated age and sex estimation.