Age estimation in adults by canine teeth: a systematic review of the Cameriere method with meta-analysis on the reliability of the pulp/tooth area ratio.

The age estimation of an adult using methods accessible to the forensic routine is a goal pursued by forensic experts. Cameriere, Ferrante and Cingolani (2004) proposed the use of the pulp/tooth area ratio of canine teeth as a promising variable, but its reliability has shown conflicting results in the scientific literature. This article aimed to carry out a systematic review with meta-analysis to verify whether the pulp/tooth area ratio of canine teeth includes a variable that can be used alone to estimate dental age in adults. A systematic search was carried out in six databases using keywords related to the theme in Portuguese, English, and Spanish. The study selection process followed pre-established eligibility criteria. Assessments were carried out regarding risk of bias and publication bias of selected studies, and meta-analysis was carried out considering Pearson's correlation coefficient between pulp/tooth area ratio and chronological age as effect measure. Most selected studies showed low risk of bias; no publication bias was found when all studies were considered, and potential publication bias was found when outliers were removed. Despite the high heterogeneity among studies and the need for more research, it could be observed that the pulp/tooth area ratio has strong negative correlation with chronological age, and the pulp/tooth area ratio could be derived from both periapical radiographs and orthopantomographs. Therefore, it is suggested that there is scientific evidence that the pulp/tooth area ratio obtained from canine teeth is reliable for dental age estimation in adults.

Exploratory analysis of new craniometric measures for the investigation of biological sex using open-access statistical and machine-learning tools on a cone-beam computed tomography sample.

Investigation of the biological sex of human remains is a crucial aspect of physical anthropology. However, due to varying states of skeletal preservation, multiple approaches and structures of interest need to be explored. This research aims to investigate the potential use of distances between bifrontal breadth (FMB), infraorbital foramina distance (IOD), nasal breadth (NLB), inter-canine width (ICD), and distance between mental foramina (MFD) for combined sex prediction through traditional statistical methods and through open-access machine-learning tools. Ethical approval was obtained from the ethics committee, and out of 100 cone beam computed tomography (CBCT) scans, 54 individuals were selected with all the points visible. Ten extra exams were chosen to test the predictors developed from the learning sample. Descriptive analysis of measurements, standard deviation, and standard error were obtained. T-student and Mann-Whitney tests were utilized to assess the sex differences within the variables. A logistic regression equation was developed and tested for the investigation of the biological sex as well as decision trees, random forest, and artificial neural networks machine-learning models. The results indicate a strong correlation between the measurements and the sex of individuals. When combined, the measurements were able to predict sex using a regression formula or machine learning based models which can be exported and added to software or webpages. Considering the methods, the estimations showed an accuracy rate superior to 80% for males and 82% for females. All skulls in the test sample were accurately predicted by both statistical and machine-learning models. This exploratory study successfully established a correlation between facial measurements and the sex of individuals, validating the prediction potential of machine learning, augmenting the investigative tools available to experts with a high differentiation potential.

GADNN: a revolutionary hybrid deep learning neural network for age and sex determination utilizing cone beam computed tomography images of maxillary and frontal sinuses.

INTRODUCTION: The determination of identity factors such as age and sex has gained significance in both criminal and civil cases. Paranasal sinuses like frontal and maxillary sinuses, are resistant to trauma and can aid profiling. We developed a deep learning (DL) model optimized by an evolutionary algorithm (genetic algorithm/GA) to determine sex and age using paranasal sinus parameters based on cone-beam computed tomography (CBCT). METHODS: Two hundred and forty CBCT images (including 129 females and 111 males, aged 18-52) were included in this study. CBCT images were captured using the Newtom3G device with specific exposure parameters. These images were then analyzed in ITK-SNAP 3.6.0 beta software to extract four paranasal sinus parameters: height, width, length, and volume for both the frontal and maxillary sinuses. A hybrid model, Genetic Algorithm-Deep Neural Network (GADNN), was proposed for feature selection and classification. Traditional statistical methods and machine learning models, including logistic regression (LR), random forest (RF), multilayer perceptron neural network (MLP), and deep learning (DL) were evaluated for their performance. The synthetic minority oversampling technique was used to deal with the unbalanced data. RESULTS: GADNN showed superior accuracy in both sex determination (accuracy of 86%) and age determination (accuracy of 68%), outperforming other models. Also, DL and RF were the second and third superior methods in sex determination (accuracy of 78% and 71% respectively) and age determination (accuracy of 92% and 57%). CONCLUSIONS: The study introduces a novel approach combining DL and GA to enhance sex determination and age determination accuracy. The potential of DL in forensic dentistry is highlighted, demonstrating its efficiency in improving accuracy for sex determination and age determination. The study contributes to the burgeoning field of DL in dentistry and forensic sciences.

The Brazilian (FRANCO) method for dental age estimation: Willems' model revisited.

OBJECTIVES: This study aimed (I) to test the Willems' dental age estimation method in different geographic samples of the Brazilian population, and (II) to propose a new model combining the geographic samples in a single reference table of Brazilian maturity scores. MATERIALS AND METHODS: The sample consisted of 5017 panoramic radiographs of Brazilian males (n = 2443) and females (n = 2574) between 6 and 15.99 years (mean age = 10.99 ± 2.76 years). The radiographs were collected from the Southeastern (SE) (n = 2920), Central-Western (CW) (n = 1176), and Southern (SO) (n = 921) geographic regions. Demirjian's technique was applied followed by Willems' method and the proposed Brazilian model. RESULTS: Willems' method led to mean absolute errors (MAE) of 0.79 and 0.81 years for males and females, respectively. Root mean squared errors (RMSE) were 1.01 and 1.03 years, respectively. The Brazilian model led to MAE of 0.72 and 0.74 years for males and females, respectively, and RMSE of 0.93 years for both sexes. The MAE was reduced in 70% of the age categories. Differences between regions were statistically (p < 0.05) but not clinically significant. CONCLUSION: The new model based on a combined population had an enhanced performance compared to Willems' model and led to reference outcomes for Brazilians. CLINICAL RELEVANCE: Assessing patients' biological development by means of dental analysis is relevant to plan orthopedic treatments and follow up. Having a combined-region statistic model for dental age estimation of Brazilian children contributes to optimal age estimation practices.

Positive identification through comparative dental analysis in mass disaster: a systematic review and meta-analysis.

PURPOSE: The study aimed to assess the probability of achieving positive identification through comparative dental analysis (CDA) and to determine the factors that influence its success rate in mass disaster scenarios. METHODS: An electronic literature search was conducted across six databases for observational studies that reported both the total number of mass disaster victims and the count of victims identified through CDA alone. A random-effect meta-analysis, using the proportion of victims identified with CDA as the effect size, was conducted alongside subgroup analyses based on the type of disaster (natural or non-natural), the disaster classification (open or closed), and the geographical region (i.e., Europe, Asia). RESULTS: The search yielded 3133 entries, out of which 32 studies were deemed eligible. Most of the studies (96.8%) presented a low risk of bias. The meta-analysis revealed a mean weighted-proportion probability of 0.32, indicating that forensic odontology could identify about one-third of the victims in a mass disaster. The probability of comparative dental identification was three times higher in closed mass disasters compared to open disasters (p < 0.05) and was higher in mass disasters occurring in North America and Europe compared to other regions (p < 0.05). CONCLUSION: The current result suggested that CDA can identify approximately 32% of a victim in a hypothetical scenario, emphasizing the integral role of teeth and forensic odontology in victim identification framework.

On the forensic relevance of tattoos: distinguishing black inks with energy dispersive spectroscopy and backscattered scanning electron microscopy.

PURPOSE: This study aimed to analyze black tattoo inks by means of energy dispersive spectroscopy and backscattered scanning electron microscopy. METHODS: The sample consisted of five types of commercial tattoo pigments of the black colour (Easy Glow™, Electric Ink™, Iron Works™, Master Ink™, and Viper™). An Energy Dispersive Spectroscopy (EDS) detector (Silicon Drift Detector - SDD - type) attached to a Scanning Electron Microscope (SEM) device (Tescan Vega3 LMU, Libusina, Czech Republic) was used. X-ray characteristic signs were detected for each tattoo ink in an interval between 0 and 2.5 keV. The electron acceleration potential in the microscope was 15 keV. Two regions were analyzed for each sample (n = 10). On each region, a micrography of backscattered electrons (BSE) was obtained. Means and standard deviations (SD) of the weight percentages (Wt%) were calculated. RESULTS: C and O were predominant, with a mean O/C ratio between 2.69 and 2.74 Wt%. Electric Ink and Master Ink were the most similar pigments, while Easy Glow was the most distinctive - with agglomerates of Al that had a concentration 25 × higher than other specimens. Other compounds detected in the sample were Cl and Cu. CONCLUSION: EDS and SEM were efficient to distinguish black tattoo inks. These are our preliminary outcomes on the use of EDS and SEM to analyze black tattoo inks. Thus, careful interpretation is necessary to avoid rash applications in human identification practice.

Digital matching of palatal rugae patterns for forensic identification in edentulous denture wearers.

Identifying edentulous victims in forensic contexts poses a significant challenge. It has been reported that having a denture to reproduce and compare Palatal Rugae (PR) patterns is crucial for identifying edentulous individuals, yet there are no validated protocols for conducting this procedure. In this study, a new method was developed and validated for plaster molding of the internal surface of upper dentures, along with a protocol involving focus-stacked photographs and reference scales. Thirty-eight edentulous subjects participated in the study, obtaining plaster models of the patients (PM), and their dentures (AM) were standardized. The AM/PM model was highlighted and photographed with ten shots in different z-focuses for each model, using standard lighting and aligning the position of the PR perpendicular to the photographic axis using tubular spirit levels and modeling clay. The images were processed using the stacking technique and analyzed by three observers through the proposed protocol using Adobe® Photoshop®. The results were analyzed based on the intra-observer and inter-observer agreement levels, with a 95% confidence interval. This study demonstrated high-precision intra-observer and inter-observer agreement (k = 1) in the matching of Palatal Rugae (PR) and maxillary morphology obtained from participants and their dentures. The protocol is simple, cost-effective, and precise. It enables standardization of the technique for obtaining plaster models, and the exposure of PR and photographic protocol minimizes the presence of artifacts in the images, thereby reducing the likelihood of errors and promoting the reproducibility of both the recording technique and the comparison of the PR.

Morphological dental age estimation of adults using the radiographic canine pulp/tooth area ratio.

The reduction of the pulpal space following the deposition of secondary dentin is a radiographically visible morphological feature associated with aging. Currently, there is no reference morphological sample for the Northern Brazilian population when it comes to the radiographically visible dental features for age estimation. This study aimed to test an existing method for age estimation based on the canine pulp/tooth area (PTA) ratio and develop a population-specific equation. The sample consisted of 100 peri-apical radiographs of Brazilian males (n=46) and females (n=54) from the Northern geographic region. The age of the sampled participants was between 18 and 72 years (mean age=45.43±14.39years). The estimated age was obtained with the Cameriere's method. A statistically significant negative (r=-0.595) association was observed between the permanent canine PTA and the chronological age (P=0.0001). A population-specific equation was structured with a 4-fold (75%×25%) cross-validation, leading to a mean absolute error of 9.59years, and root mean square error of 11.66years (r2=0.363). This study provided evidence to support the use of Cameriere's pulp/tooth area ratio for the radiographic dental age estimation of Northern Brazilian adults, especially adjusted with a population-specific equation.

Dental age estimation methods applied to Indian children and adolescents: A systematic review and meta-analysis.

Country-specific systematic reviews and meta-analyses have been proposed to compile the available literature and rank methods based on their performance for a target population. India is a country with a vast scientific literature on dental age estimation. This systematic review aimed to provide evidence to help the decision of experts regarding the method of choice for dental age estimation in India. The research protocol was registered in Open Science Framework. Literature Search was performed in Embase, LILACS, MedLine (via PubMed), SciELO, Scopus and Web of Science. Grey Literature was searched in Google Scholar and ProQuest. Observational cross-sectional studies that compared chronological and estimated ages using Demirjian (original [DEM] and Chaillet's modification [modified-DEM]) and Acharya (ACH) methods were included. JBI tool was used to assess the risk of bias. The search detected 9799 studies, out of which 56 were eligible (n=13,107 panoramic radiographs of Indian individuals). Low risk of bias was registered for 48 studies, while 8 presented a moderate risk of bias. The meta-analysis showed a standardized mean difference between chronological and estimated ages of -0.11 (95%CI: -0.29; 0.07), 0.74 (95%CI: 0.39; 1.09), and -0.01 (95%CI: -0.23; 0.22) years for DEM, modified-DEM and ACH, respectively. High heterogeneity (I2=88-93%) was observed across studies for all the methods, including subgroup analyses based on sex. This study ranked ACH, DEM and modified-DEM (from the best to the worse) performances in the Indian population.

Radiographic morphology of canines tested for sexual dimorphism via convolutional-neural-network-based artificial intelligence.

The permanent left mandibular canines have been used for sexual dimorphism when human identification is necessary. Controversy remains whether the morphology of these teeth is actually useful to distinguish males and females. This study aimed to assess the sexual dimorphism of canines by means of a pioneering artificial intelligence approach to this end. A sample of 13,046 teeth radiographically registered from 5838 males and 7208 females between the ages of 6 and 22.99 years was collected. The images were annotated using Darwin V7 software. DenseNet121 was used and tested based on binary answers regarding the sex (male or female) of the individuals for 17 age categories of one year each (i.e. 6-6.99, 7.7.99… 22.22.99). Accuracy rates, receiver operating characteristic (ROC) curves and confusion matrices were used to quantify and express the artificial intelligence's classification performance. The accuracy rates across age categories were between 57-76% (mean: 68%±5%). The area under the curve (AUC) of the ROC analysis was between 0.58 and 0.77. The best performances were observed around the age of 12 years, while the worst were around the age of 7 years. The morphological analysis of canines for sex estimation should be restricted and allowed in practice only when other sources of dimorphic anatomic features are not available.

Applying artificial intelligence to determination of legal age of majority from radiographic data.

Forensic odontologists use biological patterns to estimate chronological age for the judicial system. The age of majority is a legally significant period with a limited set of reliable oral landmarks. Currently, experts rely on the questionable development of third molars to assess whether litigants can be prosecuted as legal adults. Identification of new and novel patterns may illuminate features more dependably indicative of chronological age, which have, until now, remained unseen. Unfortunately, biased perceptions and limited cognitive capacity compromise the ability of researchers to notice new patterns. The present study demonstrates how artificial intelligence can break through identification barriers and generate new estimation modalities. A convolutional neural network was trained with 4003 panoramic-radiographs to sort subjects into 'under-18' and 'over-18' age categories. The resultant architecture identified legal adults with a high predictive accuracy equally balanced between precision, specificity and recall. Moving forward, AI-based methods could improve courtroom efficiency, stand as automated assessment methods and contribute to our understanding of biological ageing.

Legal conflicts in forensic dentistry: practice and methods of resolving them.

OBJECTIVE: Aim: To investigate and analyze legal conflicts in forensic dentistry of Ukraine. PATIENTS AND METHODS: Materials and Methods: A comparative research method for determining the common and distinctive features of the legal regulation of forensic dental expertise as a subcluster in the legislation of Ukraine. The descriptive (monographic) method reveals the problematic aspects of forensic dental examination through the lens of local normative and general normative connotations. The structural-functional research method made it possible to systematize the peculiarities of forensic medical and forensic dental expert activity. CONCLUSION: Conclusions: The analysis of legal conflicts made it possible to come to the following conclusions, that in Ukraine today there is no specific legal act that would regulate the aspects of performing dental activities. Forensic dental examination in Ukraine, in accordance with the legislation, is an examination of the actions and inactions of the dentist. The adoption of normative legal acts in the field of dental activity and forensic dental examination in Ukraine would make it possible to determine the specifics of establishing facts and circumstances that indicate a violation of the patient's rights.

Determining Whether an Individual is 18 Years or Older Based on the Third Molar Root Pulp Visibility in East China.

OBJECTIVES: To investigate the age-related changes of the mandibular third molar root pulp visibility in individuals in East China, and to explore the feasibility of applying this method to determine whether an individual is 18 years or older. METHODS: A total of 1 280 oral panoramic images were collected from the 15-30 years old East China population, and the mandibular third molar root pulp visibility in all oral panoramic images was evaluated using OLZE 0-3 four-stage method, and the age distribution of the samples at each stage was analyzed using descriptive statistics. RESULTS: Stages 0, 1, 2 and 3 first appeared in 16.88, 19.18, 21.91 and 25.44 years for males and in 17.47, 20.91, 22.01 and 26.01 years for females. In all samples, individuals at stages 1 to 3 were over 18 years old. CONCLUSIONS: It is feasible to determine whether an individual in East China is 18 years or older based on the mandibular third molar root pulp visibility on oral panoramic images.

Research Progress on Dental Age Estimation Based on MRI Technology.

Dental age estimation is a crucial aspect and one of the ways to accomplish forensic age estimation, and imaging technology is an important technique for dental age estimation. In recent years, some studies have preliminarily confirmed the feasibility of magnetic resonance imaging (MRI) in evaluating dental development, providing a new perspective and possibility for the evaluation of dental development, suggesting that MRI is expected to be a safer and more accurate tool for dental age estimation. However, further research is essential to verify its accuracy and feasibility. This article reviews the current state, challenges and limitations of MRI in dental development and age estimation, offering reference for the research of dental age assessment based on MRI technology.

Dental Age Estimation in Northern Chinese Han Children and Adolescents Using Demirjian's Method Combined with Machine Learning Algorithms.

OBJECTIVES: To investigate the application value of combining the Demirjian's method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents. METHODS: Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian's method. Various machine learning algorithms, including support vector regression (SVR), gradient boosting regression (GBR), linear regression (LR), random forest regression (RFR), and decision tree regression (DTR) were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated. RESULTS: SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error (MAE) of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old. CONCLUSIONS: The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered.

Adolescents and Children Age Estimation Using Machine Learning Based on Pulp and Tooth Volumes on CBCT Images.

OBJECTIVES: To estimate adolescents and children age using stepwise regression and machine learning methods based on the pulp and tooth volumes of the left maxillary central incisor and cuspid on cone beam computed tomography (CBCT) images, and to compare and analyze the estimation results. METHODS: A total of 498 Shanghai Han adolescents and children CBCT images of the oral and maxillofacial regions were collected. The pulp and tooth volumes of the left maxillary central incisor and cuspid were measured and calculated. Three machine learning algorithms (K-nearest neighbor, ridge regression, and decision tree) and stepwise regression were used to establish four age estimation models. The coefficient of determination, mean error, root mean square error, mean square error and mean absolute error were computed and compared. A correlation heatmap was drawn to visualize and the monotonic relationship between parameters was visually analyzed. RESULTS: The K-nearest neighbor model (R2=0.779) and the ridge regression model (R2=0.729) outperformed stepwise regression (R2=0.617), while the decision tree model (R2=0.494) showed poor fitting. The correlation heatmap demonstrated a monotonically negative correlation between age and the parameters including pulp volume, the ratio of pulp volume to hard tissue volume, and the ratio of pulp volume to tooth volume. CONCLUSIONS: Pulp volume and pulp volume proportion are closely related to age. The application of CBCT-based machine learning methods can provide more accurate age estimation results, which lays a foundation for further CBCT-based deep learning dental age estimation research.

Comparative study of cranial measurements between sexes from Brazil and The Netherlands: A cone-beam computed tomography study.

The objective of this study was to better understand human variation by comparing cone-beam computed tomography-based cranial measurements between both sexes of individuals from two distinct populations: Brazilian and Dutch. Cone-beam computed tomography volumes of 311 patients between 20 and 60 years from Brazil and The Netherlands were selected. Two radiologists performed 16 linear measurements in the maxillary sinuses and mandibular canal. Kruskall-Wallis test compared measurements of the two cranial structures between male and female for the two populations and four age ranges (20-30, 31-40, 41-50, 51-60). Mann-Whitney test compared individual measurements obtained from the cranial structures between male and female for each population, and between both populations for both sexes. Intra- and inter-observer reliability was assessed by intraclass correlation test (α = 0.05). No significant differences were found in the linear measurements among the experimental groups including sex, population and age group for both cranial structures (p > 0.05). Most of the cranial linear measurements were significantly higher for male than those for female irrespective of the population (p ≤ 0.05). When the populations were compared regardless of sex, Brazilians presented four significantly higher measurements, and Dutch presented seven significantly higher measurements (p ≤ 0.05). The assessed cranial structures did not differ between Brazilian and Dutch populations for both sexes and four age ranges. Multiple linear measurements differed between both populations with a predominance of larger dimensions for the Dutch population.

A systematic overview of dental methods for age assessment in living individuals: from traditional to artificial intelligence-based approaches.

Dental radiographies have been used for many decades for estimating the chronological age, with a view to forensic identification, migration flow control, or assessment of dental development, among others. This study aims to analyse the current application of chronological age estimation methods from dental X-ray images in the last 6 years, involving a search for works in the Scopus and PubMed databases. Exclusion criteria were applied to discard off-topic studies and experiments which are not compliant with a minimum quality standard. The studies were grouped according to the applied methodology, the estimation target, and the age cohort used to evaluate the estimation performance. A set of performance metrics was used to ensure good comparability between the different proposed methodologies. A total of 613 unique studies were retrieved, of which 286 were selected according to the inclusion criteria. Notable tendencies to overestimation and underestimation were observed in some manual approaches for numeric age estimation, being especially notable in the case of Demirjian (overestimation) and Cameriere (underestimation). On the other hand, the automatic approaches based on deep learning techniques are scarcer, with only 17 studies published in this regard, but they showed a more balanced behaviour, with no tendency to overestimation or underestimation. From the analysis of the results, it can be concluded that traditional methods have been evaluated in a wide variety of population samples, ensuring good applicability in different ethnicities. On the other hand, fully automated methods were a turning point in terms of performance, cost, and adaptability to new populations.

Dental age assessment: scoring systems and models from the past until the present-how is it presented in the court?

OBJECTIVE: The aim of this study is to evaluate the accuracy of the methods from the past and present, such as Demirjian, Cameriere, and AlQahtani, as well as to determine the most reliable method for human age estimation under the request from the courts according to the effect size from each method in age estimation. DESIGN: Four hundred eighty-three orthopantomographic images were selected from 318 patients from Centro Hospitalar Universitário Lisboa Norte aged between 6 and 15 years old. Several measurements of widths and lengths and classification of tooth development stages were performed, according to each age estimation method. The listing of patients and orthopantomographic images were consulted through SECTRA®. All data was entered and analysed using SPSS version 28. Observations' validity was verified through inter- and intraobserver validation. RESULTS: The correlation coefficients between age and age estimates by the three methods on both sides were close to 90%. Regarding the estimation error correlation coefficient, Demirjian and AlQahtani values were low, whereas Cameriere's was significantly negative; i.e., underestimation increases as age increases. Between left and right, any significant difference in age estimation in AlQahtani and Cameriere methods was not observed, but there were great variability and large effect for the Demirjian method. Comparing females and males, the statistical analysis showed no significant differences and quite small effects in the precision of the estimates for any of the methods. Finally, although significant differences were detected when comparing estimated values and age, small effects were revealed except for the Demirjian method which attained a medium effect and, therefore, less consistency of estimation. CONCLUSIONS: Since it was not possible to determine the most reliable method for age estimation, a combined application of different age estimation methodologies is recommended to be use in the courts with relevant statistical data such as effect size.

3D segmentation of dental crown for volumetric age estimation with CBCT imaging.

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.