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.

A new metric method for sex estimation using three-dimensional imaging of the nuchal crest.

In Walker's nonmetric method, the nuchal crest serves as the representative region for indicating sexual dimorphism in cranial bones. However, the accuracy of sex estimation using the nuchal crest is lower than that using other anatomical regions. Furthermore, because of the protruding processes and structurally challenging features characterized by uneven and rough surfaces, there is a lack of metric methods for sex estimation, making quantification challenging. In this study, we aimed to validate a derived metric method for sex estimation by reconstructing the nuchal crest region in three-dimensional (3D) images obtained from computed tomography scans of cranial bones and compare its accuracy with that of the nonmetric method. A total of 648 images were collected, with 100 randomly selected for use in the nonmetric method. We applied our metric method to the remaining 548 images. Our findings showed that the surface area of the nuchal crests was greater in male individuals than in female individuals. The nuchal crest surface area quantified by the metric method increased the accuracy of sex estimation by 48% compared with that by the nonmetric method. Our metric method for sex estimation, which quantifies the nuchal crest surface area using 3D images of the skull, led to a high sex estimation accuracy of 93%. Future studies should focus on proposing and quantifying new measurement methods for areas showing sexual characteristics in the skull that are difficult to measure, thereby enhancing the accuracy and reliability of sex estimation in human skeletal identification across various fields.

Enhancing Sex Estimation Accuracy with Cranial Angle Measurements and Machine Learning.

The development of current sexing methods largely depends on the use of adequate sources of data and adjustable classification techniques. Most sex estimation methods have been based on linear measurements, while the angles have been largely ignored, potentially leading to the loss of valuable information for sex discrimination. This study aims to evaluate the usefulness of cranial angles for sex estimation and to differentiate the most dimorphic ones by training machine learning algorithms. Computed tomography images of 154 males and 180 females were used to derive data of 36 cranial angles. The classification models were created by support vector machines, naïve Bayes, logistic regression, and the rule-induction algorithm CN2. A series of cranial angle subsets was arranged by an attribute selection scheme. The algorithms achieved the highest accuracy on subsets of cranial angles, most of which correspond to well-known features for sex discrimination. Angles characterizing the lower forehead and upper midface were included in the best-performing models of all algorithms. The accuracy results showed the considerable classification potential of the cranial angles. The study demonstrates the value of the cranial angles as sex indicators and the possibility to enhance the sex estimation accuracy by using them.

Sex estimation using metrics of the innominate: A test of the DSP2 method.

Sex estimation is a critical component of the biological profile, and forensic anthropologists may use a variety of sex estimation methods depending upon the degree of completeness and state of preservation of the skeletal remains being analyzed. The innominate is widely accepted to be the most sexually dimorphic skeletal element. The Diagnose Sexuelle Probabiliste (DSP) method, which uses 10 measurements of the innominate, was introduced in 2005 and updated as DSP2 in 2017. While DSP2 has been reported to have high classification accuracy rates in studies of South American and European populations, the method has not been widely tested in US samples, and few US practitioners incorporate this method into their casework. The goal of this study was to test the reliability and accuracy of DSP2 using a large, modern sample from the US (n = 174). Two observers, blinded from demographic information associated with each specimen, collected the DSP2 metrics. Intra- and interobserver error analyses showed acceptable levels of agreement for all measurements, except for IIMT. Classification accuracies exceeded 95%, with minimal sex bias, for both observers and using various measurement combinations; however, an inclusivity sex bias occurred with more males reaching the 0.95 posterior probability threshold required by DSP2 to provide a sex classification estimate. Based on its high accuracy, forensic anthropologists in the US may consider incorporating DSP2 into their casework, although we recommend excluding IIMT and using SPU with caution. Additional methods will continue to be needed when the posterior probability threshold is not reached.

Morphometric analysis of the hard palate in sex estimation among koreans using three-dimensional computed tomography.

Accurate sex estimation is crucial for comprehensive analysis of the biological profiles of unidentified human skeletal remains. However, there is a notable lack of research specifically addressing the morphometrics of the hard palate. Therefore, this study aimed to derive discriminant equations using the hard palate and assess their applicability for sexing partial skeletal remains in a contemporary Korean population. Statistical analyses were performed for 24 measurements derived from three-dimensional models of the hard palate, generated using computed tomography scans of 301 individuals (156 males, 145 females). Descriptive statistics revealed significant sexual dimorphism in the mean comparison of hard palate sizes between Korean males and females, with males exhibiting larger palates across all measurements (p < 0.05). Discriminant function score equations were generated to aid in sex determination. Univariate analysis yielded an accuracy range of 57.8-75.1%, whereas the stepwise method achieved an accuracy of 80.7% with five selected variables: IF-PNS, GFL-GFR, IF-GFR, Pr-EcL, and Pr-EnR. The results of this metric analysis demonstrate the usefulness of the hard palate for sex estimation in the contemporary Korean population. These findings have potential implications for forensic investigations, archeological studies, and population-specific anatomical research.

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.

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.

Age Estimation through Hounsfield Unit Analysis of Pelvic Bone in the Romanian Population.

Background: Bone density is affected by age- and sex-related changes in the os coxae, often known as the pelvic bone. Recent developments in computed tomography (CT) imaging have created new opportunities for quantitative analysis, notably regarding Hounsfield Units (HU). Objectives: The study aims to investigate the possibility of using HU obtained from os coxae CT scans to estimate age in the Romanian population. Methods: A statistical analysis was conducted on a sample of 80 pelvic CT scans in order to find any significant correlation between age, sex, and variation in density among the different pelvic bone locations of interest. According to the research, pelvic radiodensity measurements varied significantly between male and female participants, with men having greater levels. This technique may be valuable for determining an individual's sex precisely, as evidenced by the substantial association found between HU levels and changes in bone density associated with sex. Results: The analysis of variance underscores that HU values exhibit a significant negative relationship with radiodensity, with a general trend of decreasing HU with increasing age. The equation derived from the ordinary least squares OLS regression analysis can be used to estimate the age of individuals in the Romanian population based on their HU values at specific pelvic sites. Conclusions: In conclusion, the application of HU analysis in CT imaging of the coxae represents a non-invasive and potentially reliable method for age and sex estimation, and a promising avenue in the field of human identification.

Sex estimation using skull silhouette images from postmortem computed tomography by deep learning.

Prompt personal identification is required during disasters that can result in many casualties. To rapidly estimate sex based on skull structure, this study applied deep learning using two-dimensional silhouette images, obtained from head postmortem computed tomography (PMCT), to enhance the outline shape of the skull. We investigated the process of sex estimation using silhouette images viewed from different angles and majority votes. A total of 264 PMCT cases (132 cases for each sex) were used for transfer learning with two deep-learning models (AlexNet and VGG16). VGG16 exhibited the highest accuracy (89.8%) for lateral projections. The accuracy improved to 91.7% when implementing a majority vote based on the results of multiple projection angles. Moreover, silhouette images can be obtained from simple and popular X-ray imaging in addition to PMCT. Thus, this study demonstrated the feasibility of sex estimation by combining silhouette images with deep learning. The results implied that X-ray images can be used for personal identification.

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.

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.

Estimation of sex assigned at birth using dental crown and cervical measurements in a modern global sample.

Despite developing prior to the appearance of secondary sexual characteristics of the skeleton, the permanent dentition exhibits sexual dimorphism. Therefore, teeth can serve as a means to estimate sex assigned at birth even in young individuals. This project takes a large global sample of maximum dimensions of the crown as well as measurements of the crown at the cervix to explore sexual dimorphism. Dimorphism is noted in teeth throughout the dental arcade, particularly in the canines. We provide sectioning points as well as the probability of correct classification (ranging from 50.9% to 81.3%) for each measurement to aid the practitioner in sex estimation from the dentition. This research provides a method to estimate sex without arbitrary population specifications. We argue for a global approach that incorporates more population variation to remove the need to estimate "ancestry," (which in actuality is translated to a social race category) and therefore does not force sexual dimorphism-related variation into these mutable and ambiguous categories. Further, this paper demonstrates the utility of the dentition as an additional indicator to aid with the estimation of sex assigned at birth in forensic anthropology. The goal of this research is to better understand the expression of sexual dimorphism across the skeleton in a global context.

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.

Sexual dimorphism of pelvic scarring: A new method of adult biological sex estimation.

Estimating biological sex is a crucial aspect of forensic anthropology, and is pivotal in forensic investigations. Presently, the most frequently adopted osteological sex estimation methods focus on the anterior pelvis, which is easily susceptible to postmortem damage, revealing a need for additional accurate methods. This study introduces a novel method for estimating adult sex through metric pelvic scar analysis, using a known skeletal sample (169 females; 51 males). Relationships between sex and scar dimensions were subjected to Kendall's tau-B testing, and the strongest associated measurements were further analyzed using binary logistic regression to determine their predictive capacity. The final estimation method was tested on an additional known-sex sample of 43 males and 43 females from the Spitalfields skeletal collection. All associations between biological sex and scar measurements were significant, with the preauricular sulcus and newly defined inferior interosseous cavity presenting the strongest relationships (τb 0.223-0.504). Individual regression models using the approximate volume of each feature predicted sex with over 80% accuracy, but when combined in a single regression model, the accuracy increased to an impressive 97.1%. When then applied to the validation sample, the final estimation model achieved an accuracy of 90.7%. These results highlight the high estimation accuracy achieved by simultaneously utilizing the approximate volume of the sulcus and the inferior cavity. This is not only highly accurate but also utilizes the sturdier posterior pelvis, making it a promising tool for forensic investigations and the wider field of osteology.

Reliability of predictive models based on the mesiodistal and buccolingual diameters of permanent canine teeth for sex estimation in forensic contexts: A systematic review and meta-analysis.

This study aimed to assess the reliability of predictive models for sex estimation based on permanent canine size. A systematic literature review was performed by following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Six electronic databases were searched as the primary source of information. As a secondary source of information, a manual search was performed to identify additional relevant studies not captured in the initial search. After assessing the methodological quality and risk of bias with the Joanna Briggs Institute Critical Appraisal Tools for Systematic Reviews, the data were subjected to statistical tests for a meta-analysis of diagnostic test accuracy and Higgin's I2 statistic to evaluate the heterogeneity between the eligible studies. The systematic search resulted in 21 studies for qualitative synthesis, and 13 of them were selected for quantitative analysis. The analysis of 25 univariate predictive models showed an estimated sensitivity of 77.2 % and specificity of 67.1 %. Meta-regression analyses were performed for dental arch, the type of diameter and dental region outcomes for these univariate predictive models. Dental arch (p = 0.029) and the dental region of measurement (p = 0.001) were significant modifiers. The analysis of 25 multivariate predictive models showed an estimated sensitivity of 82.6 % and specificity of 70.1 %. There were significant methodological limitations and substantial heterogeneity among the included studies. Based on the results, there is insufficient high-quality scientific evidence to support the safe use of predictive models based on permanent canine measurements as the exclusive method for sex estimation in forensic settings.

Sex Determination via the Second Cervical Vertebra and Odontoid Process: A Case Report and a Review of the Literature.

Determining an individual's sex is crucial in several fields, such as forensic anthropology, archaeology, and medicine. Accurate sex estimation, alongside the estimation of age at death, stature, and ancestry, is of paramount importance for creating a biological profile. This profile helps narrow the potential pool of missing persons and aids identification. Our research focuses on the second cervical vertebra and odontoid process, which is particularly valuable due to their high sexual dimorphism. This brief research is structured as follows: we provide an overview of morphometric analysis of the second cervical vertebra for accurate sex estimation in forensic anthropology. We then delve into a case report to explore sexual dimorphism of the C2 vertebrae. Moreover, we discuss some of these studies that showed a significant correlation between the dimensions of the second cervical vertebrae and height, suggesting that the C2 can be used as a reliable indicator for stature estimation. The high accuracy rate of sex estimation using the second cervical vertebrae suggests that this method is a valuable tool for forensic anthropologists. Its practical application can significantly contribute to identifying and profiling individuals in a forensic context, thereby aiding in the identification process.

Sex estimation using post-mortem computed tomographic images of the clavicle in a Malaysian population.

Forensic practitioners need contemporary anthropological data for the identification of human remains. The clavicle possesses a high degree of variability in its anatomical, biomechanical, and morphological features that are sex-dependent albeit population specific. The aim of this study was to develop sex estimation models for Malaysian individuals using post-mortem computed tomographic images of the clavicle. Sample comprised scans of 2.0 mm resolution of 405 individuals (209 male; 196 female) aged between 19 to 88 years. These scans were reconstructed and visualized using Infinitt. Six clavicular measurements (i.e. maximum length, C1; midshaft circumference, C2; midshaft maximum diameter, C3; midshaft minimum diameter, C4; maximum breadth of the sternal end, C5; and maximum breadth of the acromial articular surface, C6) were obtained from these images. Data were analyzed using descriptive statistics and discriminant function analysis. Measurements taken from the images were highly precise (ICC = 0.770-0.999). There is a significant difference between all parameters and sex (p < 0.001), however none for age and ethnic group. A multivariate sex estimation model was developed: Sex = (C1*0.86) + (C2*0.236) + (C3*-0.145) + (C5*- 0.074) - 17.618; with an accuracy rate of 89.1 % and sex bias of -3.2 %. Lower accuracy rates were obtained for single variable models (61.5-83.2 %). The resultant sex discriminant models can be used for estimating sex based on the clavicle in our local forensic practice.

Deep learning in sex estimation from photographed human mandible using the Human Osteological Research Collection.

Sex estimation is a necessary part of forensic and osteological analyses of skeletal human remains in the construction of a biological profile. Several skeletal traits are sexually dimorphic and used for skeletal sex estimation. The human mandible and morphological traits therein have been long used for sex estimation, but the validity of using the mandible in this purpose has become a concern. In this study, we examined the potential of artificial intelligence (AI) and especially deep learning (DL) to provide accurate sex estimations from the mandible. We used 193 modern South African mandibles from the Human Osteological Research Collection (HORC) in the Sefako Makgatho Health Sciences university with known sex to conduct our study. All mandibles were photographed from the same angle and the photographs were analyzed with an open-source DL software. The best-performing DL algorithm estimated the sex of males with 100% accuracy and females with 76.9% accuracy. However, further studies with a higher number of specimens could provide more reliable validity for using AI when building the biological profile from skeletal remains.

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.

Sex Prediction of Hyoid Bone from Computed Tomography Images Using the DenseNet121 Deep Learning Model / Predicción del Sexo del Hueso Hioides a Partir de Imágenes de Tomografía Computarizada Utilizando el Modelo de Aprendizaje Profundo DenseNet121

SUMMARY: The study aims to demonstrate the success of deep learning methods in sex prediction using hyoid bone. The images of people aged 15-94 years who underwent neck Computed Tomography (CT) were retrospectively scanned in the study. The neck CT images of the individuals were cleaned using the RadiAnt DICOM Viewer (version 2023.1) program, leaving only the hyoid bone. A total of 7 images in the anterior, posterior, superior, inferior, right, left, and right-anterior-upward directions were obtained from a patient's cut hyoid bone image. 2170 images were obtained from 310 hyoid bones of males, and 1820 images from 260 hyoid bones of females. 3990 images were completed to 5000 images by data enrichment. The dataset was divided into 80 % for training, 10 % for testing, and another 10 % for validation. It was compared with deep learning models DenseNet121, ResNet152, and VGG19. An accuracy rate of 87 % was achieved in the ResNet152 model and 80.2 % in the VGG19 model. The highest rate among the classified models was 89 % in the DenseNet121 model. This model had a specificity of 0.87, a sensitivity of 0.90, an F1 score of 0.89 in women, a specificity of 0.90, a sensitivity of 0.87, and an F1 score of 0.88 in men. It was observed that sex could be predicted from the hyoid bone using deep learning methods DenseNet121, ResNet152, and VGG19. Thus, a method that had not been tried on this bone before was used. This study also brings us one step closer to strengthening and perfecting the use of technologies, which will reduce the subjectivity of the methods and support the expert in the decision-making process of sex prediction.

El estudio tuvo como objetivo demostrar el éxito de los métodos de aprendizaje profundo en la predicción del sexo utilizando el hueso hioides. En el estudio se escanearon retrospectivamente las imágenes de personas de entre 15 y 94 años que se sometieron a una tomografía computarizada (TC) de cuello. Las imágenes de TC del cuello de los individuos se limpiaron utilizando el programa RadiAnt DICOM Viewer (versión 2023.1), dejando solo el hueso hioides. Se obtuvieron un total de 7 imágenes en las direcciones anterior, posterior, superior, inferior, derecha, izquierda y derecha-anterior-superior a partir de una imagen seccionada del hueso hioides de un paciente. Se obtuvieron 2170 imágenes de 310 huesos hioides de hombres y 1820 imágenes de 260 huesos hioides de mujeres. Se completaron 3990 imágenes a 5000 imágenes mediante enriquecimiento de datos. El conjunto de datos se dividió en un 80 % para entrenamiento, un 10 % para pruebas y otro 10 % para validación. Se comparó con los modelos de aprendizaje profundo DenseNet121, ResNet152 y VGG19. Se logró una tasa de precisión del 87 % en el modelo ResNet152 y del 80,2 % en el modelo VGG19. La tasa más alta entre los modelos clasificados fue del 89 % en el modelo DenseNet121. Este modelo tenía una especificidad de 0,87, una sensibilidad de 0,90, una puntuación F1 de 0,89 en mujeres, una especificidad de 0,90, una sensibilidad de 0,87 y una puntuación F1 de 0,88 en hombres. Se observó que se podía predecir el sexo a partir del hueso hioides utilizando los métodos de aprendizaje profundo DenseNet121, ResNet152 y VGG19. De esta manera, se utilizó un método que no se había probado antes en este hueso. Este estudio también nos acerca un paso más al fortalecimiento y perfeccionamiento del uso de tecnologías, que reducirán la subjetividad de los métodos y apoyarán al experto en el proceso de toma de decisiones de predicción del sexo.