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Background: The utilization of segmentation method using volumetric data in adults dental age estimation (DAE) from cone-beam computed tomography (CBCT) was further expanded by using current 5-Part Tooth Segmentation (SGt5) method. Additionally, supervised machine learning modelling--namely support vector regression (SVR) with linear and polynomial kernel, and regression tree -- was tested and compared with the multiple linear regression model. Material and Methods: CBCT scans from 99 patients aged between 20 to 59.99 was collected. Eighty eligible teeth including maxillary canine, lateral incisor, and central incisor were used in this study. Enamel to dentine volume ratio, pulp to dentine volume ratio, lower tooth volume ratio, and sex was utilized as independent variable to predict chronological age. Results: No multicollinearity was detected in the models. The best performing model comes from maxillary lateral incisor using SVR with polynomial kernel (Radj² = 0.73). The lowest error rate achieved by the model was given also by maxillary lateral incisor, with 4.86 years of mean average error and 6.05 years of root means squared error. However, SGt5 demands a complex approach to segment the enamel volume in the crown section and a lengthier labour time of 45 minutes per tooth. [ABSTRACT FROM AUTHOR]

In adult dental age estimation, segmentation of dental volumetric information from different tooth parts using cone-beam computed tomography (CBCT) has proven beneficial in improving the regression model reliability. This segmentation method can be expanded in the crown part since the volumetric information in the crown is affected by attrition in the enamel and secondary dentine in the dentine and pulp chamber. CBCT scans from 99 patients aged between 20 and 60 were collected retrospectively. A total of 80 eligible teeth for each tooth type were used in this study. The enamel to dentine volume ratio (EDVR), pulp to dentine volume ratio (PDVR) and sex were used as independent variables to predict chronological age (CA). The EDVR was not affected by PDVR. The highest R2 was calculated from the maxillary canine (R2 = 0.6). The current approach in crown segmentation has proven to improve model performance in anterior maxillary teeth. [ABSTRACT FROM AUTHOR]

Radiographic methods using pulp-tooth volume ratio (PTVR) are important for dental age estimation. According to previous studies, using PTVR possesses different relationships with age in males and females but none of the studies have used a homogenous (approximately equal numbers of individuals in each age range) age distribution to assess this relationship and the effect of sex as a predictor on age estimation. This study was performed on Cone-beam computed tomography (CBCT)images of 521 left maxillary and 681 left mandibular canines of 719 subjects of Pakistani origin (368 females and 349 males) aged from 15 to 65 years. Planmeca Romexis® software was used to trace the outline of the pulp cavity and tooth and to calculate respective volumes. Subsequently, Microsoft® Office Excel 2016 was used to calculate the ratios. Regression analysis was performed to assess the relationship between PTVR factoring sex as apredictor for age estimation. The obtained results showed that including sex as predictor with maxillary PTVR (R2 = 0.46) have the highest predictive power. The relationship between maxillary PTVR including sex with chronological age demonstrates anon-linear relationship. The conclusion is that including sex as predictor with maxillary PTVR produced the best estimate of chronological age. [ABSTRACT FROM AUTHOR]

Cone‐beam computed tomography (CBCT) enables the assessment of regressive morphological changes in teeth, which can be used to predict chronological age (CA) in adults. As each tooth region is known to have different correlations with CA, this study aimed to segment and quantify the sectional volumes of the tooth crown and root from CBCT scans to test their correlations with the chronological age (CA). Seventy‐five CBCT scans from individuals with age between 20 and 60 years were collected retrospectively from an existing database. A total of 192 intact maxillary anterior teeth fulfilled the eligibility criteria. The upper tooth volume ratio (UTVR), lower tooth volume ratio (LTVR), and sex were used as predictor variables. The UTVR and LTVR parameters were both found to be differently correlated to CA and independent from each other. Regression models were derived from each tooth, with the highest R2 being the maxillary lateral incisor (R2 = 0.67). Additional single predictor models using each ratio were capable of reliably predicting the CA. The segmentation approach in volumetric adult dental age estimation proved to be beneficial in enhancing the reliability of the regression model. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the reproducibility of dental age estimation methods in cone beam computed tomography (CBCT) and the correlation between dental (DA) and chronological (CA) ages. The scientific literature was searched in six databases (PubMed, Scopus, LILACS, Web of Science, SciELO, and OATD). Only observational studies were selected. Within each study, the outcomes of interest were (I) the quantified reproducibility of the method (κ statistics and Intraclass correlation coefficient); and (II) the correlation (r) between the dental and chronological ages. A random-effect three-level meta-analysis was conducted alongside moderator analysis based on methods, arch (maxillary/mandibular), population, and number of roots. From 671 studies, 39 fulfilled the inclusion criteria, with one study reporting two different methods. The methods used in the studies were divided into metric (n = 17), volumetric (n = 20), staging (n = 2), and atlas (n = 1). All studies reported high examiner reproducibility. Group 1 (metric and volumetric) provided a high inverse weighted r ( δ ^ = −0.71, CI [-0.79,–0.61]), and Group 2 (staging) provided a medium-weighted r ( δ ^ = 0.49, CI [0.44, 0.53]). Moderator analysis on Group one did not show statistically significant differences between methods, tooth position, arch, and number of roots. An exception was detected in the analysis based on population (Southeast Asia, δ ^ = −0.89, CI [-0.94,–0.81]). There is high evidence that CBCT methods are reproducible and reliable in dental age estimation. Quantitative metric and volumetric analysis demonstrated better performance in predicting chronological age than staging. Future studies exploring population-specific variability for age estimation with metric and volumetric CBCT analysis may prove beneficial. [ABSTRACT FROM AUTHOR]