Can human sex be estimated based on the dimensions of the maxillary sinuses? A systematic review of the literature on cone-beam and multi-slice computed tomography.

PURPOSE: To critically appraise and summarize the potential of linear and/or volumetric dimensions of the maxillary sinuses obtained with cone-beam and multi-slice computed tomography. METHODS: A bibliographic search was conducted in seven databases in August 2023. Cross-sectional retrospective studies using linear and volumetric measurements of the maxillary sinuses obtained with cone-beam and multi-slice computed tomography for sex estimation and presenting numerical estimation data were included. Narrative or systematic reviews, letters to the editor, case reports, laboratory studies in animals, and experimental studies were excluded. The critical appraisal and certainty of evidence were assessed using the guidelines described by Fowkes and Fulton and GRADE, respectively. RESULTS: A total of 656 studies were found, 32 of which were included. A total of 3631 individuals were analyzed and the overall sex estimation rate ranged from 54.9 % to 95 %. When compared with isolated measurements, combined linear measurements of the right and left maxillary sinuses, such as width, length, and height, provided a higher rate of sex estimation (54.9-95 %). In most of the studies (62.5 %), all measurements were higher in men than in women. Multiple methodological problems were found in the studies, especially distorting influences in 84.4 % of the answers. The certainty of evidence varied from very low to low. CONCLUSIONS: The combination of height, width, and length measurements of the right and left maxillary sinuses from cone-beam and multi-slice computed tomography can be useful in the estimation of sex of humans. Further primary studies are needed to increase the certainty of evidence. PROSPERO REGISTER: CRD42020161922.

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.

[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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

Estimation of sex from femoral bone using radiological imaging methods in Turkish population.

Sex estimation is leading to determine the biological profile in forensic medicine. The aim of this study is to research the effectiveness of logistic regression (LogR) and discriminant function analysis (DFA) to create sex estimation models on femur images obtained with Computed Tomography (CT) angiography and to address the differences of femur, which show sexual dimorphism, among populations. All parameters were measured on three planes by adjusting the 300 CT angiography images from 150 women and 150 men that focused on the left femur to the orthogonal plane with standard magnification. The subgroup, which included 30 images randomly generated from these images, was measured twice with an interval of 3 weeks by the first radiologist and once by the second radiologist. According to the Fisher's Linear Discriminant analysis, which was evaluated with ten parameters in the study, it was concluded that the power of discriminating women was 96.7%, the power of discriminating men was 98.7%, and the total discrimination power was 97.7%; these results were 98%, 99.3%, and 98.7%, respectively according to LogR. In this study, DFA and LogR analysis showed that femur provided a very good rate of sexual dimorphism. A database belonging to the Turkish population was created for the femur, allowing for comparison between populations.