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

Fetal MRI is an important tool for the prenatal diagnosis of brain malformations and is often requested after second‐trimester ultrasonography reveals a possible abnormality. Despite the immature state of the fetal brain at this early stage, early suggestive signs of the presence of brain malformations can be recognized. To differentiate between the normal dynamics of the growing brain and the developing pathological conditions can be challenging and requires extensive knowledge of normal central nervous system developmental stages and their neuroradiological counterparts at those different stages. This article reviews the second‐trimester appearances of some commonly encountered brain malformations, focusing on helpful tricks and subtle signs to aid in the diagnosis of such conditions as rhombencephalosynapsis, various causes of vermian rotation, molar tooth spectrum anomalies, diencephalic‐mesencephalic junction dysplasia, ganglionic eminence anomalies, and the most common malformations of cortical development.

Sellar (caroticoclinoid and interclinoid), pterygospinous and pterygoalar bridges are osseous bars of the sphenoid bone, which enclose additional foramina in the skull base and could cause entrapment of nerves, occlusion of vessels and obstruction of surgical corridors. This study aimed to investigate the frequency of sphenoid bone bridges in Bulgarians and to assess the bilateral and sex differences in their distribution. This study was performed on head CT scans of 315 Bulgarians, 148 males and 167 females. The sellar bridges were the most common type of sphenoid bridging; particularly the caroticoclinoid bridge. The pterygospinous bridge was a relatively common finding and the pterygoalar bridge was the most infrequent type of bridging. The total frequency of sellar bridges did not differ significantly between both sides and sexes. The pterygospinous bridge did not indicate significant bilateral differences but showed considerable sex differences concerning the left-side occurrence, which was significantly higher in the male series. There were no considerable bilateral and sex differences in the distribution of the pterygoalar bridging. There were no significant correlations between the different types of sphenoid bone bridges, but each type of bridging showed significant positive correlations between the right and left side co-occurrence in males and females.

The pelvis is the most dimorphic part of the human skeleton, primarily because of its involvement in the birth process. Many sexually dimorphic traits are concentrated in the coxal bones, which form the larger part of the birth canal. The present study aimed to assess the sex differences in coxal bone size and to develop machine learning (ML) models for sex estimation based on coxal bone measurements. The sample included abdominal computed tomography scans of 276 adult Bulgarians. Three-dimensional models of the pelves were generated using InVesalius. The three-dimensional coordinates of 34 landmarks located on the right and left coxal bones were collected in MeshLab. Based on the landmark coordinates, various measurements characterizing the coxal bones were calculated. The coxal bone dimensions were tested for significant differences with respect to sex, age, and laterality. Support Vector Machines and logistic regression were employed to train models for sex estimation. The results demonstrate strong sexual dimorphism in coxal bone dimensions along with some bilateral and age-related differences. The trained ML models classify male and female bones with very high accuracy, ranging between 95% and 100%.

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.