Dental Age Estimation Research Articles

Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling

Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster cases. Traditionally, this process is done manually by human expert. However, the speed and accuracy may vary depending on the expertise level of the human expert and other human factors such as level of fatigue and attentiveness. To improve the recognition speed and consistency, researchers have proposed automated age estimation using deep learning techniques such as Convolutional Neural Network (CNN). CNN requires many training images to obtain high percentage of recognition accuracy. Unfortunately, it is very difficult to get large number of samples of dental images for training the CNN due to the need to comply to privacy acts. A promising solution to this problem is a technique called Generative Adversarial Network (GAN). GAN is a technique that can generate synthetic images that has similar statistics as the training set. A variation of GAN called Conditional GAN (CGAN) enables the generation of the synthetic images to be controlled more precisely such that only the specified type of images will be generated. This paper proposes a CGAN for generating new dental images to increase the number of images available for training a CNN model to perform age estimation. We also propose a pseudo-labelling technique to label the generated images with proper age and gender. We used the combination of real and generated images to train Dental Age and Sex Net (DASNET), which is a CNN model for dental age estimation. Based on the experiment conducted, the accuracy, coefficient of determination (R2) and Absolute Error (AE) of DASNET have improved to 87%, 0.85 and 1.18 years respectively as opposed to 74%, 0.72 and 3.45 years when DASNET is trained using real, but smaller number of images.

Progress in the study of four dental age inference methods in the age inference of children and adolescents

The age of the human individual is an important element of the individual's biological information. Over time, many irreversible ageing changes occur in several organs and tissues of the human body, and in forensic medicine, age interpretation has an extremely important link to the identification of identity in criminal cases, the division of sentencing in juvenile and child crime[1], and the determination of the age of lost persons in missing persons. In previous studies, bone age such as wrist bone, vertebral bone and sternum are often used for age prediction. Bone age can be used as a preliminary evaluation of age, but due to the influence of congenital genetics, racial differences and acquired environment, it may result in poorly developed bone tissue, bringing interference to the process of interpretation and eventually leading to distorted data [2]. The tooth is the hardest human tissue and its tissue structure also contributes to its extremely high stability. Compared to skeletal maturation, teeth are less likely to be altered during growth and development due to changes in the external environment [3-4], which allows it to be used more often in events that require individual identity interpretation. One of the most common methods of estimating dental age is radiological examination to assess the degree of calcification [5], and imaging of the oral cavity is also very common in modern clinical medicine. This article reviews and summarises the research progress and analyses the advantages and disadvantages of four different methods of age estimation applicable to children and adolescents, and objectively evaluates the four different methods of dental age estimation.

ESTIMATION OF AGE FROM ATTRITION OF TEETH IN DECOMPOSED DEAD BODIES- A RETROSPECTIVE STUDY

Objective:Age estimation is important in forensic medicine and odontology for identification of deceased victims and also for crimes and accidents. Various methods have been constructed and tested to estimate the age of young individuals. Among them are the physical examinations using anthropometric measurements, skeletal maturation, dental age estimation, a combination of dental development and anthropometric measurements and a combination of skeletal and tooth eruption. In cases of unknown and unclaimed decomposed bodies, age estimation from attrition of teeth is one of the important parameters. Material and Methods: In the present study, we retrospectively studied 80 cases of decomposed dead bodies centered on the attrition effect over the occlusal surface of molar teeth with the advancing age. Result and Conclusion: The attrition of teeth was mild in the young adult, moderate in middle aged and severe in old age. In this study, we concluded that the grade of attrition of teeth as per Gustafsons Method commensurate with the advancement of age of the person.

Dental Age estimation using pulp:tooth ratio of permanent maxillary central incisors: An Intraoral periapical radiographic method

Introduction: Dental age estimation can be used for various scenarios like verificationand identification of victims, in refugees and immigrants from other countries withdisputed birth records, etc. Various methods for estimation of dental age have been recognized, which include morphological, histological,radiographic and biochemical methods. The radiographic method is non-invasive and easier to perform. Aim: To compare the Pulp:Tooth ratio in permanent maxillary central incisors and correlate the actual age with that of the estimated age, using intra-oral periapical radiographs(IOPA’s) of selected cases from South Indian population. Materials and methodology: Archival intra-oral periapical radiographs of thirty adult South Indian origins (Males = 19 andFemales = 11), withan age range between 21 to 43 years were selected. All the IOPA’s were plotted in GNU image manipulation program 2.8 (GIMP) softwareand the tooth and pulp area were measured. Data was subjected to correlation and regression analysis of maxillary central incisor with actual age. Results: Gender-wise comparisonof theestimated age using pulp:tooth ratio, with that of the actual age showed it to be 28.26 ± 7.13in males and 34.27 ± 7.80in females, respectively.The pulp tooth ratio in males was 0.30 ± 0.12 and in females, it was 0.20 ± 0.00.Comparison of the estimated age using Pulp:tooth ratio, with that of the actual age was statistically significantbetween males and females.Regression analysis of the actual age with that of estimated age values using Pulp:tooth ratio,showed statistical significance (p≤0.05). Conclusion: Intraoral periapical radiographicmethodin Maxillary central incisors,can be a less expensive, non- invasive technique, which can be performed in living individuals and it can be used as an effective technique for dental age estimation. Keywords: Dental ageestimation; Intraoral periapical radiograph, Maxillary central incisor; Pulp:tooth ratio

Performance of the London Atlas, Willems, and a new quick method for dental age estimation in Chinese Uyghur children

BackgroundNumerous dental age estimation methods have been devised and practised for decades. Among these, the London Atlas and Willems methods were two of the most frequently adopted, however dependent on atlantes or tables. A new estimation method less reliant on external measurement could be efficient and economical.AimThis study aimed to evaluate the utility and applicability of the dental age estimation methods of London Atlas, Willems, and a new quick method that subtracts the number of developing teeth from the universal root mature age of 16 years in one of the lower quadrants reported in this work among Chinese Uyghur children.MethodsA comparative cross-sectional study was conducted. Subjects enrolled in the study were screened according to preset inclusion and exclusion criteria. The observer then obtained the dental age from the subjects’ panoramic radiographs based on the estimated rules of the London Atlas, Willems, and a new quick method. Paired t-test was used to compare the accuracy and precision of the above three estimation methods. Independent-sample t-test was used to find the difference between gender.ResultsTotally, 831 radiographs entered the analyses of this study. Among the three methods evaluated, the Willems method, in particular, showed a distinct underestimated tendency. The mean error of the dental age predicted by the London Atlas, the Willems method, and the quick method was 0.06 ± 1.13 years, 0.44 ± 1.14 years, and 0.30 ± 0.63 years, respectively. The mean absolute error was 0.86 ± 0.75 years according to the London Atlas, 1.17 ± 0.89 years under the Willems method, and 0.70 ± 0.54 years under our quick method. No significant difference was found between the chronological age and dental age using the London Atlas, generally for the 10 to 15 years group (p > 0.05), but our quick method for the 15–16 years children (p < 0.05) and Willems method (p < 0.001).ConclusionThe London Atlas outperformed the Willems method with better accuracy and precision among 10–15 years Chinese Uyghur children. Our new quick method may be comparable to the London Atlas for children aged 10–14 and potentially become a more straightforward dental age prediction instrument.

Dental Age Estimation using Demirjian's Methods: A Comparative study

context: There are various methods have been developed for dental age estimation, of these; Demirjian’s seven teeth method is the most tested and accepted method worldwide. It is based on tooth development rather than tooth eruption, because the developmental process is strictly controlled by genes and less influenced by dietary insufficiency, endocrinal disturbances, and environmental insults. aims: The purpose of this study is to evaluate the applicability of two Demirjian’s methods in a sample of the Varanasi region. setting and design: This is a cross-sectional prospective study. material and methods: The evaluation of digital panoramic images of a total of 432 children and adolescents (237 boys and 195 girls) of age rang 3-16 years from the population of the Varanasi region. Seven left mandibular teeth excluding the third molar were rated and scored to obtain dental age using Dem73 and Dem76 methods. statistical analysis: Paired t-test was applied to determine the significant difference between estimated dental age and chronological age and correlation was established using Pearson’s correlation coefficient. results: The Dem73 method overestimated the dental age in boys by 0.23 (±0.80) years and by 0.12 (±0.85) years in the total sample. In the girls’ sample, there was no discernible difference in mean age. In boys, girls, and the total sample, the Dem76 method overestimated dental ages by 0.85 (±1.14), 0.45 (±1.00), and 0.67 (±1.10) years, respectively. Pearson’s correlation revealed a high coefficient value between dental and chronological ages. conclusions: Dem73 method is more applicable than the Dem76 method for age estimation among children and adolescents in the Varanasi region.

Reference and target sample age distribution impacts between model types in dental developmental age estimation

The performance of age estimation methods may vary due to a combination of method- and sample-related factors. Method development and evaluation necessitates understanding what influences these factors have on age estimation outcomes. In the specific context of juvenile dental age estimation, we used a single dataset and complete factorial design to systematically test four potential sources of difference: age distributions of reference and target sample (uniform, unimodal, U-shaped), Bayesian (multivariate Bayesian cumulative probit) vs. classical regression modeling (multivariate adaptive regression splines i.e. MARS), and model selection bias. The dataset consisted of 850 sets of left mandibular molar scores from London children 5-18years old. True age and estimated age intervals in target samples were compared for bias, root-mean-squared error, precision, and accuracy using locally weighted smoothing of performance measures across the age range and means of performance metrics between factor-level combinations. We found interactions of model type, reference distribution, and target distribution. MARS models showed consistent evidence of age mimicry. Central tendency of the reference sample corresponded with increased bias while central tendency of the target sample corresponded with reduced RMSE and reduced precision for both model types. We found evidence of model selection bias, mitigated through averaging model metrics. We conclude that reference and target sample distribution influences and model selection bias are sufficient to cause difference in model performance within a single population. We suggest using Bayesian modeling, drawing uniform reference and target samples, and calculating test error on a hold-out sample to mitigate these challenges in method development.

Evaluation of a machine learning algorithms for predicting the dental age of adolescent based on different preprocessing methods.

Machine learning (ML) algorithms play a key role in estimating dental age. In this study, three ML models were used for dental age estimation, based on different preprocessing methods. The seven mandibular teeth on the digital panorama were measured and evaluated according to the Cameriere and the Demirjian method, respectively. Correlation data were used for decision tree (DT), Bayesian ridge regression (BRR), k-nearest neighbors (KNN) models for dental age estimation. An accuracy comparison was made among different methods. We analyzed 748 orthopantomographs (392 males and 356 females) from eastern China between the age of 5 and 13 years in this retrospective study. Three models, DT, BRR, and KNN, were used to estimate the dental age. The data in ML is obtained according to the Cameriere method and the Demirjian method. Coefficient of determination (R2), mean error (ME), root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE), the above five metrics were used to evaluate the accuracy of age estimation. Our experimental results showed that the prediction accuracy of dental age was affected by ML algorithms. MD, MAD, MSE, RMSE of the dental age predicted by ML were significantly decreased. Among all the methods, the KNN model based on the Cameriere method had the highest accuracy (ME = 0.015, MAE = 0.473, MSE = 0.340, RMSE = 0.583, R2 = 0.94). The results show that the prediction accuracy of dental age is influenced by ML algorithms and preprocessing method. The KNN model based on the Cameriere method was able to infer dental age more accurately in a clinical setting.

Fully automated method for dental age estimation using the ACF detector and deep learning

BackgroundDental age estimation plays an important role in identifying an unknown person. In forensic science, estimating age with high accuracy depends on the experience of the practitioner. Previous studies proposed classification of tooth development of the mandibular third molar by following Demirjian’s method, which is useful for dental age estimation. Although stage of tooth growth is very helpful in assessing age estimation, it must be performed manually. The drawback of this procedure is its need for skilled observers to carry out the tasks precisely and reproducibly because it is quite detailed. Therefore, this research aimed to apply computer-aid methods for reducing time and subjectivity in dental age estimation by using dental panoramic images based on Demirjian’s method. Dental panoramic images were collected from persons aged 15 to 23 years old. In accordance with Demirjian’s method, this study focused only on stages D to H of tooth development, which were discovered in the 15- to 23-year age range. The aggregate channel features detector was applied automatically to localize and crop only the lower left mandibular third molar in panoramic images. Then, the convolutional neural network model was applied to classify cropped images into D to H stages. Finally, the classified stages were used to estimate dental age.ResultsExperimental results showed that the proposed method in this study can localize the lower left mandibular third molar automatically with 99.5% accuracy, and training in the convolutional neural network model can achieve 83.25% classification accuracy using the transfer learning strategy with the Resnet50 network.ConclusionIn this work, the aggregate channel features detector and convolutional neural network model were applied to localize a specific tooth in a panoramic image and identify the developmental stages automatically in order to estimate the age of the subjects. The proposed method can be applied in clinical practice as a tool that helps clinicians to reduce the time and subjectivity for dental age estimation.

Age estimation of children based on open apex measurement in the developing permanent dentition: an Egyptian formula

BackgroundCameriere’s original formula based on open apex measurements is a reliable, clinically applicable method for dental age estimation in different populations children. Dental development may differ between Egyptian children and other ethnic populations which may affect dental age accuracy using Cameriere’s formula.AimFirstly, to verify Cameriere’s original formula on large Egyptian children sample, secondly, to develop an Egyptian-specific formula based on Cameriere’s method.Material and methodsA prospective cross-sectional study of 762 good quality Orthopantomograms (OPGs) of 5–15 aged healthy Egyptian children selected from Nile Delta governorates between August 2020 and December 2021. Chronological age (CA) was calculated by subtracting birth date from radiograph date. OPGs were analyzed for N0, S, Xi morphologic variables using Sidexis program after that dental age was calculated using Cameriere’s formula then compared to CA. Multiple linear regression model was used to adapt Cameriere’s formula to construct an Egyptian formula. The same sample was used to verify the new formula accuracy.ResultsA total of 1093 OPGs were collected; 762 OPGs which met inclusion criteria were analyzed. Cameriere’s original formula revealed − 0.59- and − 0.53-year underestimation of females and males dental age (DA) respectively (p < 0.001). Regression analysis using the morphologic variables showed that X4, X7, N0 contributed significantly to CA yielding Egyptian-specific formula. New formula showed − 0.12-year male underestimation and 0.1-year female overestimation (p > 0.05).ConclusionEgyptian formula was more accurate than Cameriere’s formula in Egyptian children.Clinical relevanceEgyptian-specific formula decreases the gap between CA and DA, so a relative approximate age is obtained that helps proper diagnosis and treatment planning for orthodontic and pediatric dentistry problems.

The Effects of Experimental Whole-Body Burning on Histological Age-at-Death Estimation from Human Cortical Bone and Dental Cementum.

Whole-body donations (n = 6) were placed in various experimental fire-death scenarios to understand the histological effects of thermal alteration on bones and teeth. Midshaft samples of the femur, 6th rib, and metatarsal were removed from each donor pre- and post-burning to examine histomorphometric differences and test established age-at-death estimation methods. Dental samples were taken post-burning to test the applicability of dental cementum analysis for age-at-death estimation. Significant differences in osteon area or Haversian canal area between some pre- and post-burn samples were found although no patterns related to temperature or element were observable. The femoral age estimates across pre- and post-burn samples were 91% accurate across all donors. The point age estimates from the ribs compared to known age were significantly different (t(10) = 6.88, p &lt; 0.001) with an average difference of -18.53 years. Dental age estimates of post-burn samples were not significantly different from the known donor age (t(3) = -0.74, p = 0.512) with an average difference of -3.96 years. Overall, the results of this study show that thermally altered remains can be used for histologic age-at-death analysis of cortical bone and dental cementum, within certain burning parameters.

Age Estimation Using Maxillary Central Incisor Analysis on Cone Beam Computed Tomography Human Images.

Forensic dentistry plays an important role in human identification, and dental age estimation is an important part of the process. Secondary dentin deposition throughout an individual’s lifetime and consequent modification in teeth anatomy is an important parameter for age estimation procedures. The aim of the present study was to develop regression equations to determine age in adults by means of linear measurements and ratios on sagittal, coronal and axial slices of maxillary central incisors using cone bean computed tomography (CBCT). Multiplanar measurements of upper central incisors were taken for a sample of 373 CBCTs. Subsequently, one-way analysis of variance (ANOVA) and multivariate linear regressions were performed for age estimation. The equations obtained from axial linear measurements and ratios presented a standard error of the estimate (SEE) of ±10.9 years (R2 = 0.49), and a SEE of ±10.8 years (R2 = 0.50), respectively. The equation obtained for multiplanar linear measurements presented a SEE of ±10.9 years (R2 = 0.52), while the equation for multiplanar ratios presented a SEE of ±10.7 years (R2 = 0.51). Thus, CBCT measurements on upper central incisors were found to be an acceptable method for age estimation. Horizontal measurements, especially pulp measurements, improve the accuracy of age estimate equations.

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.

Adapting Demirjian Standards for Portuguese and Spanish Children and Adolescents.

Estimation of children’s chronological age is highly important in human and forensic sciences. The Demirjian method has been reported as accurate for this purpose. The literature review shows some evidence that the accuracy of estimating chronological age via the Demirjian standards is not a straightforward process. The objective of this research is to analyze the reliability of the Demirjian standards in Portuguese and Spanish children and adolescents and adapt it to include sex and group age as contingent factors. Methods: Orthopantomographs of 574 Portuguese and Spanish male and female children and adolescents were employed to test the reliability of the Demirjian method. After testing for inter-rater consistency and age estimation using the Demirjian standards, multiple regression analysis was performed controlling for sex and age group. Results: The Demirjian standards overestimated chronological age for both sexes, mainly for females. Through the development of regression functions, more detailed dental age estimation was performed. The predictive capacities of the Demirjian method and the significant teeth varied as a function of children’s age. The Demirjian global standard predicted over 65% of the variance of the chronological age. Taking a tooth-by-tooth approach, the predictive ability increased by over 70%. Conclusions: The accuracy of estimating chronological age via the Demirjian method is not as reliable as it might appear, judging from the results found according to age group and according to sex crossed with age group.

Comparing the accuracy of Demirjian and Nolla methods and establishing a new method for dental age estimation in northeastern Chinese children

Dental age estimation plays an important role in the field of clinic medicine and forensic medicine. The Demirjian and Nolla methods are common scoring methods for dental age estimation but there was no research about the comparison of accuracy of these two methods in northeastern Chinese children. Hence, in this study, we compared the accuracy of these two methods to explore more suitable method for our studied population. We collected 535 orthopantomograms from northern Chinese children aged from 6 to 15 years and divided them into training dataset and testing dataset according to the ratio of 7:3. The dental age of training dataset were estimated using Demirjian and Nolla methods, respectively. The results suggested that the mean differences of these two methods were 0.24 and −0.40 years, and mean absolute difference were 0.65 and 0.59 years. Then to further improve the accuracy of dental age assessment, the new improved formulas and dental age conversion tables were established after analyzing the relationship between the sum scores based on Nolla method and chronology age in training dataset. According to the new method used in testing dataset, the minimum value of mean difference (0.00) and mean absolute difference (0.49) were obtained, which are largely smaller than that of Demirjian and Nolla methods. The new developed method and dental age conversion scales may be more suitable dental age estimation method for northeastern Chinese children.

Demirjian’s dental age estimation accuracy among Ukrainian. Transcarpathian children sample: pilot study

Background. Number of forensic methods have been proposed for dental age estimation, while parameter of dental age itself is demonstrating a high level of correspondence with chronological age. Analysis of available literature revealed deŢciency of studies regarding applicability of using Demirjian’s age estimation technique among Ukrainian samples for forensic purpose.&#x0D; Objective. To evaluate accuracy of original Demirjian’s dental age estimation method amongsamp le of Ukrainian Transcarpathian children.&#x0D; Materials and Methods. Study was organized as retrospective by the design based on the analysis of 276 digital panoramic X-ray images obtained from the patients of University Dental Clinic aged 6.0-15.99 years old. Dental age estimation was held by the originally proposed Demirjian’s technique.&#x0D; Results. Dental age estimation using Demirjian’s technique provoked overestimation effect on 0.43 years among patients aged 6.0-6.99 years, on 0.49 years among patients aged 7.0-7.99 years, on 0.47 years among patients aged 8.0-8.99 years, on 0.55 years among patients aged 9.0-9.99 years, on 0.51 years among patients aged 10.0-10.99 years, on 0.55 years among patients aged 11.0-11.99 years, on 0.44 years among patients aged 12.0-12.99 years, on 0.45 years among patients aged 13.0-13.99 years, on 0.54 years among patients aged 14.0-14.99 years, on 0.58 years among patients aged 15.0-15.99 years.&#x0D; Conclusion. Demirjian’s dental age estimation technique may be used for forensic purposes among population of children from Ukrainian Transcarpathia, if accuracy of other available dental age estimation methods would not be validated till the moment of needed forensic investigation.

Examining the Deciduous Tooth Eruption and Carpal Osteogenesis in Relation to Forensic age Estimation

Objective: To compare the accuracy of deciduous teeth eruption and ossification of carpel bone of hand for estimation of forensic age. Study Design: Randomized controlled trial Place and Duration: Department of Forensic, Bacha Khan Medical college Mardan from January 2022 to June 2022. Methodology: Total 140 children of both genders with ages 5 to 15 years were enrolled in this study. All the patients were equally divided into two groups, each group consist of 70 patients. In group A dental age estimates were made using radioscopic (RVG) pictures of the left quadrant mandibular teeth by Demirjian method and the right hand wrist radiograph by Greulich and Pyle method was used for the estimation of the age of the skeleton in Group B. Statistical analysis on differences between chronological age and approximate skeletal and dental age was carried out. Results: There were 40 (57.14%) male and 30 (42.86%) females in group A and in group B 38 (54.29%) male and 32 (45.71%) females. The difference between chronological age and dental age in children with ages 5 to 10 years was 0.64±1.24 years and children with ages 11 to 15 years the difference was 0.67±0.18 years in group A and in group B difference between chronological age and skeletal age among children with age group 5 to 10 years was 0.72±1.18 years and among children with ages 11 to 15 years the difference was 0.66±1.18 years. No significant difference was observed between both groups with p-value &gt;0.05. Conclusion: It is concluded that both procedures dental age estimation and skeletal age estimation are effective for forensic age estimation. No significant difference was observed between both methods. Keywords: Age Estimation, Forensic, Dental Age (Demirjian’s), Bone Age (Greulich and Pyle)

Boosting algorithm improves the accuracy of juvenile forensic dental age estimation in southern China population

Age estimation based on the mineralized morphology of teeth is one of the important elements of forensic anthropology. To explore the most suitable age estimation protocol for adolescents in the South China population, 1477 panoramic radiograph images of people aged 2–18 years in the South were collected and staged by the Demirjian mineralization staging method. The dental ages were estimated using the parameters of the Demirjian and Willems. Mathematical optimization and machine learning optimization were also performed in the data processing process in an attempt to obtain a more accurate model. The results show that the Willems method was more accurate in the dental age estimation of the southern China population and the model can be further optimized by reassigning the model through a nonintercept regression method. The machine learning model presented excellent results in terms of the efficacy comparison results with the traditional mathematical model, and the machine learning model under the boosting framework, such as gradient boosting decision tree (GBDT), significantly reduced the error in dental age estimation compared to the traditional mathematical method. This machine learning processing method based on traditional estimation data can effectively reduce the error of dental age estimation while saving arithmetic power. This study demonstrates the effectiveness of the GBDT algorithm in optimizing forensic age estimation models and provides a reference for other regions to use this parameter for age estimation model establishment, and the lightweight nature of machine learning offers the possibility of widespread forensic anthropological age estimation.

Employing the London Atlas in the Age Estimation of a Select South African Population.

Dental age estimation in the living and deceased is a fundamental aspect of forensic sciences, civil cases, medico-legal proceedings and clinical dentistry. Accordingly, this study aimed to validate the accuracy and reproducibility of the London Atlas in a select South African sample of KwaZulu-Natal. In this cross-sectional study, 760 digital panoramic radiographs (n = 760) aged between 5.00 and 23.99 years were retrospectively reviewed through consecutive sampling. Each radiograph was assessed and assigned a dental age in accordance with the London Atlas of Human Tooth Development and Eruption by AlQahtani et al. (2010). The London Atlas overestimated age with a mean difference of −0.85 to −1.26 years in the selected South African sample of KwaZulu-Natal. A statistically significant difference between the chronological and estimated dental ages was recorded. Furthermore, the South African Black and Indian males had a higher overestimation of age than their female counterparts, with a mean difference of 0.13 and 0.07 years, respectively. This overestimation was less in the South African Indian population in comparison to the SA Black population. This outcome resulted in the creation of the KZN population- and sex-specific charts and atlases for the two selected cohorts of KwaZulu-Natal. The KZN Atlases were found to be more accurate in the selected sample, with a mean absolute error of 0.57 years and no statistically significant differences between the chronological and estimated dental ages.

Estimation of Dental Age Using the Kvaal Method with Digital Panoramic Radiographs in Peruvian Adults

To determine the accuracy of the Kvaal method in the estimation of dental age through the analysis of digital panoramic radiographs of patients between 20 and 47 years of age who attended the stomatological clinic of the Universidad Científica del Sur, (2018 to 2019), Lima-Perú. 289 digital panoramic radiographs belonging to individuals of both sexes, aged 20 to 47 years were analyzed. Length and width measurements were obtained in the dental and pulp chamber in the upper central incisors. These data were subsequently evaluated by calculating the proportions between the pulp and root length of the upper central incisor by applying the formula of the method of Kvaal et al. to obtain the accuracy of the method in estimating dental age. Applying the formula of Kvaal et al. indicates that the relationship between the width of the pulp and the length of the root has a higher level of significance in relation to others proportions: M mean value of all ratios except T (-0.659) and W-L difference between W and L (-0.643). The coefficient of determination (r²) and standard error of estimation (SEE) using the original Kvaal formula is r²: 0.70, SEE: 4.90 years, then a modified Kvaal formula was proposed for the Peruvian population. The Method of Kvaal et al. has an accuracy of 1.24 in estimating the dental age of individuals, using the upper central incisor in digital panoramic radiographs.