Objectives:The aim of this study was to investigate the applicability of Drusini’s and Cameriere’s methods to a sample of Turkish people.Methods:Panoramic images of 200 individuals were allocated into two groups as study and test groups and examined by two observers. Tooth coronal indexes (TCI), which is the ratio between coronal pulp cavity height and crown height, were calculated in the mandibular first and second premolars and molars. Pulp/tooth area ratios (ARs) were calculated in the maxillary and mandibular canine teeth. Study group measurements were used to derive a regression model. Test group measurements were used to evaluate the accuracy of the regression model. Pearson’s correlation coefficients and regression analysis were used.Results:The correlations between TCIs and age were −0.230, −0.301, −0.344 and −0.257 for mandibular first premolar, second premolar, first molar and second molar, respectively. Those for the maxillary canine (MX) and mandibular canine (MN) ARs were −0.716 and −0.514, r...

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.

As the teeth are more durable than other parts of the skeleton, they provide valuable data for age estimation. Age estimation from adult teeth is mainly based on secondary dentin production. The present study aimed to devise a regression formula for age estimation specific to the Anatolian population using the Kvaal method on CBCT reconstructed panoramic images. In total, 201 individuals aged between 20 and 69 were divided into two groups: data from the study group (n = 101) were used to create the regression formulae, and data from the control group (n = 100) were used to test the formulae. Pearson’s correlation coefficients and linear regression analyses were performed. Maxillary teeth provided more accurate age estimates than mandibular teeth. The regression formulae derived in this study are found to be statistically applicable and reasonably accurate. However, these results should be interpreted with caution.

Background/Objectives: This study aimed to evaluate the relationship between CBCT-derived total pulp volume and chronological age in mandibular first molars (M1) and second molars (M2), and to explore the preliminary utility of volumetric pulp analysis as a supplementary indicator for age-related dental changes in a Turkish population sample. Methods: CBCT scans of 60 individuals (32 females, 28 males; age range: 18–48 years) were retrospectively analysed. Pulp volumes of 60 mandibular molars (32 first and 28 second molars) were segmented using a semi-automatic thresholding approach with manual correction. Spearman correlation analysis was performed to assess the relationship between pulp volume and chronological age, stratified by sex and tooth type. Following comprehensive regression diagnostics—including the Shapiro–Wilk test, Breusch–Pagan test, and Cook’s distance—Huber M-estimator robust regression was selected as the primary analytical method, with OLS bootstrapped regression retained as a sensitivity analysis. Results: A statistically significant negative correlation was found between pulp volume and chronological age in both mandibular first molars (Spearman ρ = −0.554, p = 0.001) and second molars (ρ = −0.693, p < 0.001). Huber robust regression confirmed pulp volume as a significant predictor in both groups: M1 (β = −0.151, p = 0.001; Age = 33.646 − 0.151 × Volume) and M2 (β = −0.205, p < 0.001; Age = 36.833 − 0.205 × Volume). OLS sensitivity analysis yielded R² = 0.238 (SEE = ±5.978 years, n = 32) for M1 and R² = 0.499 (SEE = ±4.908 years, n = 28) for M2. No statistically significant sex differences were detected in either molar group. Conclusions: This preliminary study suggested that CBCT-derived mandibular molar pulp volumetry demonstrates a statistically significant negative association with chronological age in a predominantly young adult Turkish population.