In forensics, predicting the sex is a crucial step in identification. Many studies have aimed to find an accurate and fast technique to estimate the sex. This study was conducted to determine the accuracy of volumetric and linear measurements of three-dimensional (3D) images of the mandible obtained from cone beam computed tomography (CBCT) radiographs, using different machine-learning (ML) models for sex identification. The CBCTs of 104 males and 104 females were included in this study. The radiographs were converted to 3D images, and the volume, surface area, and ten linear measurements of the mandible were obtained. The data were evaluated using statistical analysis and five different ML algorithms. All results were considered statistically significant at p < 0.05, and the precision, recall, f1-score, training accuracy, and testing accuracy were used to evaluate the performance of the ML models. All the studied parameters showed statistically significant differences between sexes p < 0.05. The right coronoid-to-gonion linear distance had the highest discriminative power of all the parameters. Meanwhile, Gaussian Naive Bayes (GNB) showed the best performance among all the ML models. The results of this study revealed promising outcomes; the sex can be easily determined, with high accuracy (90%).

A study was carried out to analyze the efficacy and practical application for age estimation using digital panoramic radiograph to exploit image analysis to obtain metric measurement of morphological parameters of permanent mandibular first molar on Sulaimani population. In the present study a population of known age and sex was studied and subjected to digital panoramic radiographic examination. The correlation between the reduction of coronal pulp cavity and chronological age was examined in a sample of 96 individuals distributed into four age groups: 20-29 years (29 cases), 30-39 years (29 cases), 40-49 years (26 cases) and 50-59 years (12 cases). The height (mm) of the crown (CH = coronal height) and the height (mm) of coronal pulp cavity (CPCH = coronal pulp cavity height) of 96 of first molars from all subjects was measured. The tooth-coronal index (TCI) after Ikeda et al. was computed for each tooth and regressed on real age. ANOVA was used to show the strength of relation between the age and TCI (P = 0.0000). The correlation coefficient (r(2)) was 0.49, which mean there is strong negative linear regression between age and TCI with the r (2), regarding predicting age using TCI value, after the following equation calculated, Predicted age = 3.78 - (0.064 TCI) showed that there is no significant difference between real age and estimated age. There is a strong negative liner relationship between TCIs of mandibular first molars with chronological age of Sulaimani population, and age of individuals can therefore be estimated with a good degree of accuracy using regression equations.

In circumstances where antemortem information concerning the deceased individual is unavailable, forensic experts prepare biological profiling for unidentified human remains that aids in narrowing the search for identity. Biological profiling includes basic demographic information such as sex, age, stature, and ethnicity. Sex identification is the first and key step in the biological profiling of unidentified human remains, as it effectively reduces potential matches by excluding nearly one-half of the suspected cases and facilitates the subsequent stages. This study was conducted to assess the accuracy of artificial intelligence (AI) in predicting sex by analysing mandibular canine dimensions, mandibular intercanine distance (MICD), and mandibular canine index (MCI) obtained from three-dimensional (3D) digital impressions captured by using an intraoral scanner (IOS). The results of the receiver operating characteristic (ROC) test indicated that mean mandibular canine width (MeanMCW) had the highest sexual dimorphism with the area under the curve (AUC) of 0.912, and the Gaussian Naive Bayes (GNB) classifier demonstrated the highest testing accuracy among all machine learning (ML) models, achieving an accuracy of 92.5%. While the outcomes of this study are promising, further studies are imperative to validate these findings with larger sample sizes in different ethnic populations.

Forensic dentistry is crucial in human identification, with dental age estimation being a key component of this process. The deposition of secondary dentin over an individual’s lifetime, along with the resulting changes in tooth anatomy, serves as an important factor in age estimation. The objective of this study was to develop regression equations for estimating age in adults based on linear measurements and ratios obtained from axial, sagittal, and coronal planes of the maxillary central incisors in the Iraqi subpopulation using cone beam computed tomography (CBCT). In this retrospective cross-sectional study, Multiplanar measurements of 400 maxillary central incisors from subjects ranging in age from 18 to 84 years were taken for a sample of 200 CBCTs. The data were analyzed using independent Student’s t-test, Mann-Whitney test, and Spearman’s and Pearson’s correlation coefficients to determine the strength of correlations. Multiple regression analysis was performed to predict age, and a p-value of ≤ 0.05 was considered statistically significant. The age range of the participants was 18–84 years. The results revealed a statistically significant difference in age between male and female samples (p = 0.018). Consequently, separate age estimation equations were developed for each sex. In males, the equation derived from multiplanar linear measurements demonstrated a standard error of estimate (SEE) of ± 10.84 years, with a coefficient of determination (R²) of 0.49. In contrast, the equation for females, based on multiplanar measurements, yielded a SEE of ± 11.11 years and an R² value of 0.393. Based on the findings of this study, dental morphometric measurement of maxillary central incisors using CBCT was found to be an acceptable method for age estimation especially for identification of unknow human remains. Horizontal measurements improve the accuracy of age estimate equations.

This study evaluated the prevalence and eruption’s pattern of impacted mandibular third molars (IMTM) and the influence of their eruption status on the distal caries of mandibular second molars (MSM) using cone-beam computed tomography (CBCT). Material and methods: CBCT images taken for different purposes in private dental practices were analyzed retrospectively. Radiographic assessment included: prevalence of IMTM, degree of angulation, level of impaction and type of IMTM. Furthermore, the distance between the cement-enamel junctions (CEJ) of second and third molars and the occurrence of caries lesion on the distal surface of MSM was also evaluated. Data were analyzed by chi square test and logistic regression was used to find the association between distal caries of MSM and eruption status of IMTM. Results: Three hundred and eight CBCTs were screened, the prevalence of IMTM was 36.88% and their angulation degree were mostly less than 90º (mesioangular). Amongst those with impaction, 58 subjects (43%) had distal caries on MSM, 29.6% in females and 30.4% in the age group 19-27 years. Caries on the distal side of MSM were significantly associated with age, level and type of impaction, angulation degree and CEJ distances (p<0.05). Conclusions: The prevalence of IMTM is high (36.88%) and there are significant relationships between angulation degree, level and type of impaction, and CEJ distances with caries on the distal side of MSM.