OBJECTIVE: 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.
UNASSIGNED: CRD42020161922.

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*β) 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.

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.

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.
OBJECTIVE: 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, İnönü 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.

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.
Отождествление личности по общегрупповым признакам не теряет актуальности на протяжении длительного периода. Анализ публикаций (2000—2023), посвященных возможностям использования грудины для установления пола и возраста, показал достаточно большой объем наработок по данной тематике с перспективными итогами. Тренд развития этого направления — использование современных методов медицинской визуализации — становится отправной точкой для проведения подобных исследований на территории Российской Федерации с целью разработки методики опознания неустановленного лица с учетом популяционных особенностей российского населения.

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.

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.
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.
CONCLUSIONS: 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.

OBJECTIVE: 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.\"
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.

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.

OBJECTIVE: Gender determination is pivotal in establishing a biological profile of human remains, where fragments of the skull persist with unidentifiable dental arch data. Owing to the resistance and stability against external factors such as trauma and fractures, radiological assessment of the frontal sinus can be a useful indicator for sexual dimorphism. Keeping this in mind, a study was designed to analyse the efficacy of morphometric assessment of the frontal sinus for gender determination in the North Indian population.
METHODS: A total of 300 (150 males and 150 females) lateral cephalograms of the age >20 years were retrieved. The frontal sinus index (ratio of maximum height to depth), area, and perimeter of the same were calculated using Adobe Photoshop software. The results obtained were further subjected to statistical analysis.
CONCLUSIONS: The study yielded an accuracy rate of 75.3% for sex differentiation. The frontal sinus perimeter was a novel parameter which was utilised as a variable in the obtained discriminant equation for sex differentiation. Thus, this cost-effective technique might be useful as an adjunct to assess sexual dimorphism.