Anthropometric study of the scapula in a contemporary population from granada. Sex estimation and glenohumeral osteoarthritis prevalence.

Anthropometric studies of the scapula have been rare in Spanish populations, nevertheless they are of current interest in forensic anthropology for estimation of sex. Although the estimation of sex is usually carried out on the pelvis and skull, other measurements related to the scapula can be helpful when the skeletal remains are incomplete. Glenohumeral osteoarthritis development is influenced, among others, by the morphology of the scapula, which is one of the less studied aspects. We carried out a descriptive study of anthropometric parameters in a series of 157 scapulae (82 individuals) on bone remains dated to the 20th century from a population of Granada (Southern Spain). Seventy seven (49%) were right-side and 80 (51%) left-side; 72 (45.9%) were from males and 85 (54.1%) from females, and the mean age at death was 70.76±11.7 years. The objective was to develop a discrimination function for sex estimation based on anthropometric parameters of the scapula other than those considered to date, and to analyze the prevalence of glenohumeral osteoarthritis in relation to selected anthropometric parameters. A logistic regression model based on parameters of the upper-external segment of the scapula was done. The obtained formula: 1/1+e^ (- (-57.911 + 0.350*B + 0283*C + 0.249*b + 0.166*a +-0.100*ß) classifies male sex with 98.3% accuracy and female sex with 92.1%. Glenohumeral osteoarthritis was detected in 16.6% of individuals and was related to age (p<0.05), scapular length (p<0.05), glenoid width (p<0.05), glenopolar angle (p<0.05), and α angle (p<0.05) in bivariate analyses but showed no significant associations in multivariate analyses. This approach can be useful for anthropological-forensic identification when scapula remains are incomplete. Glenohumeral osteoarthritis is significantly associated with a smaller α angle.

Gender Estimation from Morphometric Measurements of Mandibular Lingula by Using Machine Learning Algorithms and Artificial Neural Networks.

BACKGROUND: Sex determination from the bones is of great importance for forensic medicine and anthropology. The mandible is highly valued because it is the strongest, largest and most dimorphic bone in the skull. AIM: Our aim in this study is gender estimation with morphometric measurements taken from mandibular lingula, an important structure on the mandible, by using machine learning algorithms and artificial neural networks. METHODS: Cone beam computed tomography images of the mandibular lingula were obtained by retrospective scanning from the Picture Archiving Communication Systems of the Department of Oral, Dental and Maxillofacial Radiology, Faculty of Dentistry, Inönü University. Images scanned in Digital Imaging and Communications in Medicine (DICOM) format were transferred to RadiAnt DICOM Viewer (Version: 2020.2). The images were converted to 3-D format by using the 3D Volume Rendering console of the program. Eight anthropometric parameters were measured bilaterally from these 3-D images based on the mandibular lingula. RESULTS: The results of the machine learning algorithms analyzed showed that the highest accuracy was 0.88 with Random Forest and Gaussian Naive Bayes algorithm. Accuracy rates of other parameters ranged between 0.78 and 0.88. CONCLUSIONS: As a result of the study, it is thought that mandibular lingula-centered morphometric measurements can be used for gender determination as well as bones such as the pelvis and skull as they were found to be highly accurate. This study also provides information on the anatomical position of the lingula according to gender in Turkish society. The results can be important for oral-dental surgeons, anthropologists, and forensic experts.

[Morphology of sternum for formation of the biological profile of unidentified individual]. / Morfologiya grudiny dlya formirovaniya biologicheskogo profilya neopoznannogo individa.

Identification of a person by general group characteristics does not lose its relevance over a long period. An analysis of publications (2000-2023) devoted to the possibilities of using the sternum to determine gender and age showed a fairly large amount of work on this topic, with very promising results. The trend in the development of this area is the use of modern methods of medical imaging. This becomes the starting point for conducting such studies on the territory of the Russian Federation and developing a methodology that includes the Russian population, taking into account their population characteristics.

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.

Sexual dimorphism of the twelfth thoracic vertebra for sex determination in the Central Thai population.

Analyzing skeletal remains is crucial for identifying individuals, and forensic anthropologists use this analysis to determine biological characteristics, particularly sex, aiding criminal investigations. Among thoracic vertebrae, the twelfth thoracic vertebra (T12) is highly sexually dimorphic in various populations. This study aims to establish a discriminant function equation (DFE) for sex determination based on T12 in the Central Thai population. A total of 15 parameters of T12 were examined in 69 bone samples (43 males and 26 females). Among the 15 parameters, 14 were significantly different between males and females. The discriminant function equation (DFE) was generated as DFE = -19.578 + 0.376(i) BDsm + 0.254(l) PW + 0.081TDm, with a cutoff value of -0.296 for males and females, showing 92.8 % accuracy. The evaluation of the DFE using 10 blind samples showed 90 % accuracy. These findings may offer an additional method for sex determination through T12, complementing the examination of other skeletal elements.

Artificial intelligence in age and sex determination using maxillofacial radiographs: A systematic review.

In the past few years, there has been an enormous increase in the application of artificial intelligence and its adoption in multiple fields, including healthcare. Forensic medicine and forensic odontology have tremendous scope for development using AI. In cases of severe burns, complete loss of tissue, complete or partial loss of bony structure, decayed bodies, mass disaster victim identification, etc., there is a need for prompt identification of the bony remains. The mandible, is the strongest bone of the facial region, is highly resistant to undue mechanical, chemical or physical impacts and has been widely used in many studies to determine age and sexual dimorphism. Radiographic estimation of the jaw bone for age and sex is more workable since it is simple and can be applied equally to both dead and living cases to aid in the identification process. Hence, this systematic review is focused on various AI tools for age and sex determination in maxillofacial radiographs. The data was obtained through searching for the articles across various search engines, published from January 2013 to March 2023. QUADAS 2 was used for qualitative synthesis, followed by a Cochrane diagnostic test accuracy review for the risk of bias analysis of the included studies. The results of the studies are highly optimistic. The accuracy and precision obtained are comparable to those of a human examiner. These models, when designed with the right kind of data, can be of tremendous use in medico legal scenarios and disaster victim identification.

Are computed tomography images of the mandible useful in age and sex determination? A forensic science meta-analysis.

OBJECTIVES: This meta-analysis addresses the use of mandibular computed tomography (CT) scans for age and/or sex determination in forensic science. METHODS: Six databases were searched until June 2023, using the keyword "mandible" combined with keywords related to "multislice computed tomography" (MCT) or "cone-beam computed tomography" (CBCT) and keywords related to "skeletal age determination" or "sex determination analysis." MAIN RESULTS: Among the 23 studies included, 11 used MCT and 12 used CBCT to perform forensic assessments. Age determination was the aim of a single study, sex and agedeterminations were the objective of five studies, and the other studies investigated the determination of sex only. Metaanalysis could be performed only for sex determination. CONCLUSIONS: Mandible measurements are useful in sex determination, as the bicondylar and bigonial breadth are larger in males than in females. For the mandible angle, the meta-analysis results confirm sex dimorphism in CBCT scans but not in MCT scans. For age estimation, further studies are needed to prove that the mandible hole is a reliable parameter for age estimation. PROSPERO registration number: CRD42021260967.

Automatic segmentation and classification of frontal sinuses for sex determination from CBCT scans using a two-stage anatomy-guided attention network.

Sex determination is essential for identifying unidentified individuals, particularly in forensic contexts. Traditional methods for sex determination involve manual measurements of skeletal features on CBCT scans. However, these manual measurements are labor-intensive, time-consuming, and error-prone. The purpose of this study was to automatically and accurately determine sex on a CBCT scan using a two-stage anatomy-guided attention network (SDetNet). SDetNet consisted of a 2D frontal sinus segmentation network (FSNet) and a 3D anatomy-guided attention network (SDNet). FSNet segmented frontal sinus regions in the CBCT images and extracted regions of interest (ROIs) near them. Then, the ROIs were fed into SDNet to predict sex accurately. To improve sex determination performance, we proposed multi-channel inputs (MSIs) and an anatomy-guided attention module (AGAM), which encouraged SDetNet to learn differences in the anatomical context of the frontal sinus between males and females. SDetNet showed superior sex determination performance in the area under the receiver operating characteristic curve, accuracy, Brier score, and specificity compared with the other 3D CNNs. Moreover, the results of ablation studies showed a notable improvement in sex determination with the embedding of both MSI and AGAM. Consequently, SDetNet demonstrated automatic and accurate sex determination by learning the anatomical context information of the frontal sinus on CBCT scans.

Age and sex estimation in cephalometric radiographs based on multitask convolutional neural networks.

OBJECTIVES: Age and sex characteristics are evident in cephalometric radiographs (CRs), yet their accurate estimation remains challenging due to the complexity of these images. This study aimed to harness deep learning to automate age and sex estimation from CRs, potentially simplifying their interpretation. STUDY DESIGN: We compared the performance of 4 deep learning models (SVM, R-net, VGG16-SingleTask, and our proposed VGG16-MultiTask) in estimating age and sex from the testing dataset, utilizing a VGG16-based multitask deep learning model on 4,557 CRs. Gradient-weighted class activation mapping (Grad-CAM) was incorporated to identify sex. Performance was assessed using mean absolute error (MAE), specificity, sensitivity, F1 score, and the area under the curve (AUC) in receiver operating characteristic analysis. RESULTS: The VGG16-MultiTask model outperformed the others, with the lowest MAE (0.864±1.602) and highest sensitivity (0.85), specificity (0.88), F1 score (0.863), and AUC (0.93), demonstrating superior efficacy and robust performance. CONCLUSIONS: The VGG multitask model demonstrates significant potential in enhancing age and sex estimation from cephalometric analysis, underscoring the role of AI in improving biomedical interpretations.

Sex estimation in the South-East Asian population: A systematic review.

INTRODUCTION: Sex estimation is crucial in forensic anthropology. In situations such as mass disasters, and forensic anthropology cases, sex estimation is a very important initial step in the disaster victim identification process. Literature has acknowledged that sex estimation is population-specific. However, sex estimation standards in South-East Asian populations are limited, leading to the usage of most Thais discriminant function equations on sex estimation by other South-East Asian countries including Malaysia. This systematic review was conducted to summarise the findings of sex estimation studies in South-East Asian countries. MATERIALS AND METHODS: A systematic literature search was performed through the SCOPUS database and Web of Science (WOS) database for relevant studies between 2014 and 2022. All published articles that are related to sex estimation from different types of bone, methods, landmarks, and sample sources (i.e., photographs, dry bones, and CT images) were included in this review. The main inclusion criteria were studies on (i) sex estimation; (ii) in South-East Asian populations; (iii) between the years 2014 and 2022; and (iv) in English. RESULTS: The literature search identified 30 potentially relevant studies, of which 15 publications met all the inclusion criteria. From those research, 13 studies were related to the Thai population and two to the Malaysian population. Only one study was based on morphological traits, while the rest were based on a morphometric approach. CONCLUSION: All studies found that sex estimation is populationspecific. Therefore, further research is recommended to explore more on population-specific sex estimation using different parts of bone.

Comparison of mandibular morphometric parameters in digital panoramic radiography in gender determination using machine learning.

OBJECTIVE: This study aimed to evaluate the usability of morphometric features obtained from mandibular panoramic radiographs in gender determination using machine learning algorithms. MATERIALS AND METHODS: High-resolution radiographs of 200 patients aged 20-77 (41.0 ± 12.7) were included in the study. Twelve different morphometric measurements were extracted from each digital panoramic radiography included in the study. These measurements were used as features in the machine learning phase in which six different machine learning algorithms were used (k-nearest neighbor, decision trees, support vector machines, naive Bayes, linear discrimination analysis, and neural networks). To evaluate the reliability, we have performed tenfold cross-validation and we repeated this 10 times for every classification process. This process enhances the reliability of the results for other datasets. RESULTS: When all 12 features are used together, the accuracy rate is found to be 82.6 ± 0.5%. The classification accuracies are also compared using each feature alone. Three features that give the highest accuracy are coronoid height (80.9 ± 0.9%), condyle height (78.2 ± 0.5%), and ramus height (77.2 ± 0.4%), respectively. When compared to the classification algorithms, the highest accuracy was obtained with the naive Bayes algorithm with a rate of 84.0 ± 0.4%. CONCLUSION: Machine learning techniques can accurately determine gender by analyzing mandibular morphometric structures from digital panoramic radiographs. The most precise results are achieved by evaluating the structures in combination, using attributes obtained from applying the MRMR algorithm to all features.

Sex estimation from maxillofacial radiographs using a deep learning approach.

The purpose of this study was to construct deep learning models for more efficient and reliable sex estimation. Two deep learning models, VGG16 and DenseNet-121, were used in this retrospective study. In total, 600 lateral cephalograms were analyzed. A saliency map was generated by gradient-weighted class activation mapping for each output. The two deep learning models achieved high values in each performance metric according to accuracy, sensitivity (recall), precision, F1 score, and areas under the receiver operating characteristic curve. Both models showed substantial differences in the positions indicated in saliency maps for male and female images. The positions in saliency maps also differed between VGG16 and DenseNet-121, regardless of sex. This analysis of our proposed system suggested that sex estimation from lateral cephalograms can be achieved with high accuracy using deep learning.

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 research trends in forensic anthropology between 2000 and 2022 in five prominent journals.

In forensic anthropology, osteological sex estimation methods are continuously reevaluated and updated to improve classification accuracies. Therefore, to gain a comprehensive understanding of recent trends in sex estimation research in forensic anthropology, a content analysis of articles published between 2000 and 2022 in Forensic Science International, the Journal of Forensic Sciences, the International Journal of Legal Medicine, the American Journal of Biological Anthropology, and Forensic Anthropology, was performed. The main goals of this content analysis were to (1) examine trends in metric versus morphological research, (2) examine which areas of the skeleton have been explored, (3) examine which skeletal collections and population affinities have been most frequently utilized, and (4) determine which statistical methods were commonly implemented. A total of 440 articles were coded utilizing MAXQDA and the resulting codes were exported for analysis. Statistical analyses were conducted utilizing the Cochran-Armitage and Jonckheere-Terpstra tests for trends, as well as Fisher-Freeman-Halton tests. The results demonstrate that sex estimation research published in these journals has prioritized metric over morphological methods. Further, the most utilized skeletal regions continue to be the skull and pelvis, while the most popular classification statistics continue to be discriminant function analysis and logistic regression. This study also demonstrates that a substantial portion of research has been conducted utilizing U.S. and Europe-based collections and limited populations. Based on these results, future sex estimation research must continue exploring the use of long bones and other postcranial elements, testing newer methods of analysis, as well as developing population-inclusive methods.

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.

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.

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.

The influence of biological relatedness on sexual dimorphism and sex classification based on external morphology of the frontal bone.

The most significant sexual differences in the human skull are located in the upper third of the face (the frontal bone), which is a useful research object, mainly in combination with virtual anthropology methods. However, the influence of biological relatedness on sexual dimorphism and frontal bone variability remains unknown. This study was directed at sexual difference description and sex classification using the form and shape of the external surface of the frontal bones from a genealogically documented Central European osteological sample (nineteenth to twentieth centuries). The study sample consisted of 47 cranial CT images of the adult members of several branches of one family group over 4 generations. Three-dimensional virtual models of the frontal bones were analyzed using geometric morphometrics and multidimensional statistics. Almost the entire external frontal surface was significantly different between males and females, especially in form. Significant differences were also found between this related sample and an unrelated one. Sex estimation of the biologically related individuals was performed using the classification models developed on a sample of unrelated individuals from the recent Czech population (Cechová et al. in Int J Legal Med 133: 1285 1294, 2019), with a result of 74.46% and 63.83% in form and shape, respectively. Failure of this classifier was caused by the existence of typical traits found in the biologically related sample different from the usual manifestation of sexual dimorphism. This can be explained as due to the increased degree of similarity and the reduction of variability in biologically related individuals. The results show the importance of testing previously published methods on genealogical data.

Estimation of sex using dimensions around the metatarsal diaphyseal nutrient foramen: Application of discriminant function analysis and logistic regression models.

Sex estimation equations are population-specific, and a wider use of multiple bones to generate equations will increase the accuracy of sex estimation in forensic settings. The metatarsal bones have been used previously, however the dimensions around the diaphyseal nutrient foramen have not been utilised in sex estimation. The current study aimed to determine the utility of the dimensions around the nutrient foramen of metatarsal bones in estimating sex in the South Africans of European descent (SAED). Five measurements around the nutrient foramen were taken from a total of 876 metatarsal bones (first to fifth) from 186 individual skeletons (99 males, 87 females) obtained from the Raymond A. Dart Modern Skeletal Collection. Measurements subjected to direct and stepwise discriminant function (DFA) and logistic regression (LRA) analyses included total length, distance from proximal end to nutrient foramen, circumference, and mediolateral and dorsoplantar diameters at the level of the nutrient foramen. The original classification accuracies for multivariable functions of the stepwise and direct DFA ranged from 83.1-88.3% to 85.5-88.3%, respectively. The original classification accuracies for multivariable functions of the stepwise and direct LRA ranged from 83.3%-88.7% to 86.2%-88.3%, respectively. The cross-validation classifications showed a drop of 0-2.4% for DFA and 0.2-1.1% for LRA. The width measurements were better predictors of sex than length. The dimensions around the metatarsal bone nutrient foramen exhibit sexual dimorphism in the SAED. The generated DFA and LRA functions produced high average classification accuracies which are useful in sex estimation during forensic human identification.

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.