Objectives: Machine learning (ML) algorithms are a portion of artificial intelligence that may be used to create more accurate algorithmic procedures for estimating an individual’s dental age or defining an age classification. This study aims to use ML algorithms to evaluate the efficacy of pulp/tooth area ratio (PTR) in cone-beam CT (CBCT) images to predict dental age classification in adults. Methods: CBCT images of 236 Turkish individuals (121 males and 115 females) from 18 to 70 years of age were included. PTRs were calculated for six teeth in each individual, and a total of 1416 PTRs encompassed the study dataset. Support vector machine, classification and regression tree, and random forest (RF) models for dental age classification were employed. The accuracy of these techniques was compared. To facilitate this evaluation process, the available data were partitioned into training and test datasets, maintaining a proportion of 70% for training and 30% for testing across the spectrum of ML algorithms employed. The correct classification performances of the trained models were evaluated. Results: The models’ performances were found to be low. The models’ highest accuracy and confidence intervals were found to belong to the RF algorithm. Conclusions: According to our results, models were found to be low in performance but were considered as a different approach. We suggest examining the different parameters derived from different measuring techniques in the data obtained from CBCT images in order to develop ML algorithms for age classification in forensic situations.

Coronavirus disease 2019 (COVID‑19) continues to affect dental emergency services worldwide. Dental anxiety (DA) is described as a common and distressing problem in terms of oral health maintenance. The present study aimed to evaluate DA levels as well as the COVID‑19 fear and perception of control (COVID‑19 FPC) in patients attending dental emergency clinics during the COVID‑19 pandemic. Sociodemographic, dental and medical data was obtained from the participants. A face-to-face questionnaire with questions referring to the reasons for the emergency dental visit, the visual pain scale, the Modified Dental Anxiety Scale (MDAS), and the COVID‑19 Fear and Perception of Control Scale (COVID‑19 FPCS) as well as additional questions concerning bruxism and a previous diagnosis of anxiety/panic attacks or depression was administered. The χ2 test was used to analyze the data. A total of 1,439 patients were included in the study. The most common reason for the dental visit was pain (47.5%). The prevalence of DA was 5.1% (74/1,439). A significant association was found between DA and gender (p = 0.020). The incidence of severe pain was higher in patients with DA than in those without DA (p = 0.002). No significant differences in the MDAS scores were found between patients with and without a chronic disease (p = 0.804), with regard to the educational status (p = 0.364), or between the age groups (p = 0.600). The prevalence of a 'strongly agree' response to all questions in COVID‑19 FPCS was higher in patients with DA as compared to those without DA. Females and patients with severe pain were more likely to exhibit DA. In general, patients with DA strongly agreed with the statements of COVID‑19 FPCS, which may indicate a correlation between the 2 scales.

This study aims to determine whether and how the data of the medial sigmoid depression (MSD) area via cone beam computed tomography (CBCT) differs from panoramic radiography. This study also aims to evaluate various sigmoid notch types and assess the relationship between sigmoid depression and notch morphology. A total of 129 individuals consisting of 258 sides were evaluated. Chi-Square/Fisher Exact tests were used to assess parameters on a categorical scale between two or more groups. McNemar’s test compared the findings detected on panoramic and CBCT images. MSD was more prevalent in females than males in both techniques, but this difference was not statistically significant. There was no association between the prevalence of MSD and the morphology of the sigmoid notch. The incidence of MSD shape was not significantly different between both imaging modalities. In both panoramic and CBCT, we found a high and similar prevalence of MSD. While the MSD prevalence was 66.7% for CBCT, it was 58.1% for panoramic. The shape or prevalence of MSDs in either approach did not correlate with sigmoid notch morphology. The two approaches' identical prevalence indicates that the panoramic image has adequately defines MSD. The high prevalence of MSD demonstrated how important it is for clinicians to characterize this anatomical variation accurately for the surgical treatment.

Objective This study aims to characterize the imaging features of the gubernaculum dentis in impacted or unerupted permanent teeth. It also investigates whether these features differ between individuals with eruption-related pathologies and those without associated pathological conditions. Materials and Methods A case-control design was employed, comprising 110 individuals in the study group (pathology-associated impacted/unerupted teeth) and 112 individuals in the control group (impacted/unerupted teeth without pathology). Categorical variables were analyzed using Pearson’s Chi-Square test, which is appropriate for evaluating associations between categorical variables. In instances where multiple group comparisons were necessary, the Bonferroni-corrected Z-test was applied to adjust for the increased risk of Type I error due to multiple testing, thereby maintaining the overall statistical validity. Results A statistically significant association was observed between GD status (absent, present, or enlarged) and group classification (pathology vs. control; p < 0.001). Specifically, GD absence was more prevalent in the pathology group (53.6%) compared to controls (26.1%). No significant age-related differences were detected between genders (p = 0.307). Conclusion The findings demonstrate an inverse relationship between GD presence and pathological conditions associated with impacted or unerupted teeth. This suggests that GD absence or enlargement may serve as a radiographic marker for underlying odontogenic pathologies. Further studies are warranted to elucidate the mechanistic role of GD in eruption disturbances.

Objective: This study aimed to investigate the applicability of fractal analysis for evaluating trabecular bone microarchitecture in multiple myeloma patients while implementing an automated macro-based workflow to provide reproducible and standardized post-processing analysis with reduced operator-dependent variability. Methodology: The study included 41 patients with multiple myeloma and 41 age- and sex-matched healthy controls. Fractal dimension measurements were initially performed manually using the conventional ImageJ/FracLac box-counting method. Subsequently, a macro-based automated workflow was evaluated for reproducibility and standardization of the analysis process. Results: No statistically significant differences were observed between the multiple myeloma and control groups for ROI1 (1.5329 ± 0.043 vs. 1.5368 ± 0.040, p = 0.674), ROI2 (1.5320 ± 0.042 vs. 1.5322 ± 0.041, p = 0.979), or ROI3 (1.5361 ± 0.049 vs. 1.5371 ± 0.048, p = 0.928). Intra-observer reliability was excellent (ICC = 0.91, 95% CI: 0.857–0.949), and agreement between manual and automated fractal dimension measurements was perfect. Conclusions: Fractal dimension analysis did not show statistically significant differences between multiple myeloma patients and controls in this cohort, suggesting limited discriminative value of the method for structural assessment of multiple myeloma under the present conditions. However, the macro-focused workflow was compatible with manual measurements and may provide a standardized and repeatable framework that could help reduce operator-induced variability in image processing and analysis, thereby supporting methodological consistency in future imaging research.

Objective: This study aimed to evaluate mandibular premolar (p) and molar (m) teeth using cone-beam computed tomography (CBCT)-derived panoramic reconstructions, with particular emphasis on premolars, which are often poorly visualized on conventional panoramic radiographs. By overcoming these imaging limitations, CBCT may allow more accurate assessment of pulp cavity morphology and improve the applicability of the Drusini method.Methods: This retrospective study evaluated CBCT images of 101 individuals (52 women, 49 men; mean age: 37.4 ± 14.3 years, range: 18–71 years). Intra-observer reliability was assessed using intraclass correlation coefficients (ICC). Sex differences were examined with independent samples t-tests. The tooth coronal index (TCI) of mandibular premolars and molars was measured using Drusini’s method. Associations between TCI and chronological age were analyzed using Pearson correlation and linear regression. Model accuracy was assessed using coefficient of determination (R²) and standard error of estimate (SEE).Results: Intra-observer reliability was good to excellent, with ICC values ranging from 0.895 to 0.929. No significant sex differences were observed for any index (p > .05). All four indices showed significant negative correlations with chronological age, with correlation coefficients ranging from Pearson correlation coefficient (r) = -0.354 to -0.411 (p < .001 for all). Simple linear regression models explained 12.0% to 16.9% of the variance in age. The combined first mandibular premolar- first mandibular molar model demonstrated the highest predictive performance (R² = 0.228, adjusted R² = 0.218; SEE = 12.7 years), representing a moderate improvement over single index model.Conclusion: Despite significant correlations, the Drusini TCI method does not provide sufficient accuracy for standalone forensic age estimation in the Turkish population and should be considered only as a complementary approach.