Radiological Evaluation of the Accuracy of Demirjian, Nolla, and Willems Methods for Dental Age Estimation in 3-17-Year-Old Iranian Children.

Background: The stage of tooth formation is one of the most reliable indicators for predicting a patient's developmental age by radiographs. This study compared the accuracy of three distinct dental age estimation methods (Demirjian, Nolla, and Willems) in children aged 3-17 in the northern Iranian population. Methods: This cross-sectional study examined panoramic radiographs of 434 children aged 3-17 from Mazandaran Province, Iran, who had teeth 31-37 present on the left mandible. This study employed the Demirjian, Nolla, and Willems methods to estimate the dental age of the sample and compare it with the chronological age. The data were analyzed using SPSS v16. A paired t-test was used to compare chronological and dental ages. The Pearson correlation was used to correlate the chronological and dental ages. The errors of different methods were compared using the Wilcoxon test. P values < 0.05 were considered significant for all tests except Wilcoxon. For Wilcoxon, a P value < 0.017 was considered significant. Results: The three methods presented differing mean estimated ages. The Demirjian method delivered the highest mean, and all three methods differed significantly when compared in pairs. The results showed that the Demirjian method overestimated chronological age by 0.25 years (P < 0.001) in girls and 0.09 years (P = 0.28) in boys. The Willems method underestimated chronological age by 0.05 years (P = 0.47) in girls and 0.12 years (P = 0.13) in boys. The Nolla method underestimated chronological age by 0.41 years (P < 0.001) in girls and 0.40 years (P < 0.001) in boys. The accuracy of each method varied with the patient's age. Conclusion: According to the findings, the Willems method outperformed the Demirjian method, and the Demirjian method exceeded the Nolla method for estimating dental age in Iranian children aged 3-17. Overall, the Demirjian method overestimated the age of the study population, whereas the other two underestimated it.

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.

Fully automated deep learning approach to dental development assessment in panoramic radiographs.

BACKGROUND: Dental development assessment is an important factor in dental age estimation and dental maturity evaluation. This study aimed to develop and evaluate the performance of an automated dental development staging system based on Demirjian's method using deep learning. METHODS: The study included 5133 anonymous panoramic radiographs obtained from the Department of Pediatric Dentistry database at Seoul National University Dental Hospital between 2020 and 2021. The proposed methodology involves a three-step procedure for dental staging: detection, segmentation, and classification. The panoramic data were randomly divided into training and validating sets (8:2), and YOLOv5, U-Net, and EfficientNet were trained and employed for each stage. The models' performance, along with the Grad-CAM analysis of EfficientNet, was evaluated. RESULTS: The mean average precision (mAP) was 0.995 for detection, and the segmentation achieved an accuracy of 0.978. The classification performance showed F1 scores of 69.23, 80.67, 84.97, and 90.81 for the Incisor, Canine, Premolar, and Molar models, respectively. In the Grad-CAM analysis, the classification model focused on the apical portion of the developing tooth, a crucial feature for staging according to Demirjian's method. CONCLUSIONS: These results indicate that the proposed deep learning approach for automated dental staging can serve as a supportive tool for dentists, facilitating rapid and objective dental age estimation and dental maturity evaluation.

Correlation between dental and skeletal maturity in Korean children based on dental maturity percentile: a retrospective study.

BACKGROUND: The correlation between dental maturity and skeletal maturity has been proposed, but its clinical application remains challenging. Moreover, the varying correlations observed in different studies indicate the necessity for research tailored to specific populations. AIM: To compare skeletal maturity in Korean children with advanced and delayed dental maturity using dental maturity percentile. DESIGN: Dental panoramic radiographs and cephalometric radiographs were obtained from 5133 and 395 healthy Korean children aged between 4 and 16 years old. Dental maturity was assessed with Demirjian's method, while skeletal maturity was assessed with the cervical vertebral maturation method. Standard percentile curves were developed through quantile regression. Advanced (93 boys and 110 girls) and delayed (92 boys and 100 girls) dental maturity groups were defined by the 50th percentile. RESULTS: The advanced group showed earlier skeletal maturity in multiple cervical stages (CS) in both boys (CS 1, 2, 3, 4, and 6) and girls (CS 1, 3, 4, 5, and 6). Significant differences, as determined by Mann-Whitney U tests, were observed in CS 1 for boys (p = 0.004) and in CS 4 for girls (p = 0.037). High Spearman correlation coefficients between dental maturity and cervical vertebral maturity exceeded 0.826 (p = 0.000) in all groups. CONCLUSION: A correlation between dental and skeletal maturity, as well as advanced skeletal maturity in the advanced dental maturity group, was observed. Using percentile curves to determine dental maturity may aid in assessing skeletal maturity, with potential applications in orthodontic diagnosis and treatment planning.

Dental and skeletal maturation of Chinese male children with unilateral cleft lip and palate.

The aim of this study was to determine whether the relationship between dental age (DA), cervical stage (CS) and chronological age (CA) in Chinese male children with unilateral cleft lip and palate (UCLP) is similar to that of children without clefts. Panoramic and cephalometric radiographs of 105 male UCLP patients, aged from 8 to 16 years, were collected and compared to 210 age-matched healthy control males. The Demirjian and cervical vertebral maturation (CVM) methods were used to visually examine the radiographs and Spearman's correlation analysis was used to identify differences between the two groups with regards to CS, DA and CA. There was a significant positive correlation between DA and CA in both groups and the mean CA-DA difference was significantly higher in children with UCLP when compared to controls (0.319 vs. 0.003, p < 0.05). A significant delay in tooth development was detected in UCLP children from 10 to 12 years-of-age. Both the UCLP and control groups showed high correlations between CS and DA. Calcification stage D appeared only before CS3; however, from CS5 to 6, all teeth have almost completed their maturation phase. Chinese male UCLP patients are likely to experience delayed tooth development compared to healthy controls, especially during the fast-growing period. Evaluating the stages of tooth mineralization could represent a rapid method to assess growth potential.

Relationship between skeletal maturity indicators and dental calcification stages in a sample pediatric population.

Context: Human growth shows considerable variation. In addition to constitutional differences in the timing of maturation, there are diseases, systemic disorders and environmental factors that may affect a child's physiologic maturity. Interrelationships among skeletal, somatic and sexual maturity have been shown to be consistently strong. The study was conducted to evaluate the relationship between dental calcification stages and skeletal maturity indicators. Materials and Methods: The hand wrist radiographs, the lateral cephalogram and the orthopantomogram (OPG) of 120 patients (60 males and 60 females, age group 8-14 years) were taken. The hand wrist radiographs were analyzed using Fishman's skeletal maturity index (SMI), and the cephalograms analyzed by cervical vertebrae maturation (CVM) by Baccetti. OPG was analyzed according to the Demirjian's system. Result: On assessment, cervical vertebrae maturation indicator (CVMI) staging showed significant results with a correlation coefficient of 0.716 being the highest in canines in males while assessment of dental calcification stages and its comparison with skeletal maturity indicator showed insignificant results. The correlation coefficient values were 0.11, 0.09, 0.09 and 0.13 for canine, first premolar, second premolar and second molar respectively. Conclusion: There is a strong correlation between the maturation stage of cervical vertebrae and calcification stages of canine, first premolar, second premolar and second molar as determined by Demirjian's method.

Tiempo empleado para estimar edad dental en niños peruanos comparando el método de Demirjian y el cuadro integral del enfoque de Demirjian: un estudio piloto / Time spent to estimate dental age in peruvian children comparing the demirjian method and the comprehensive chart based on demirjian method: a pilot study

El objetivo de este estudio fue comparar el tiempo empleado al estimar la edad dental (ED) entre el método propuesto por Demirjian et al. y el cuadro integral del enfoque de Demirjian (DAEcc) utilizando radiografías panorámicas digitales de individuos peruanos de 5 a 13 años residentes en Lima. Se realizó un estudio no experimental, comparativo, transversal y retrospectivo. Se utilizaron 100 radiografías panorámicas digitales que presentaban siete dientes permanentes mandibulares izquierdos. Una odontóloga forense capacitada y calibrada determinó el tiempo utilizado en evaluar la maduración dental y la estimación de ED con dos métodos (Demirjian y DAEcc). La unidad de medida utilizada fue los minutos (min.). No hubo diferencias en el tiempo de evaluación del estadio de maduración dental entre ambos métodos (p<0,05). El tiempo para estimar ED fue estadísticamente inferior con DAEcc (2,09 min) que con Demirjian (4,19 min). La diferencia del tiempo total de evaluación resultó estadísticamente significativa entre ambos métodos (Δ2,1 min.; 2,05-2,11; p=0,000). La aplicación del DAEcc redujo en 50 % el tiempo empleado en estimar ED en comparación con los cuadros propuestos por Demirjian. Aunque la odontología forense se centra en el estudio de la eficacia de estimación de la edad, es necesario abordar también su uso práctico.

SUMMARY: The objective of this study was to compare the time used to estimate dental age (DA) between the method proposed by Demirjian et al., and the comprehensive chart for dental age estimation (DAEcc) using digital panoramic radiographs of Peruvian individuals aged 5 to 13 years residing in Lima. A non- experimental, comparative, cross-sectional and retrospective study was carried out. 100 digital panoramic radiographs showing seven mandibular left permanent teeth were used. A trained and calibrated forensic odontologist determined the time used to assess tooth maturation and DA estimation with two methods (Demirjian and DAEcc). The unit of measurement used was minutes (min.). There were no differences in the evaluation time of the dental maturation stage between both methods (p<0.05). The time spent to estimate DA was statistically less with DAEcc (2.09 min) than with Demirjian (4.19 min). The difference in total evaluation time was statistically significant between both methods (Δ2.1 min; 2.05-2.11; p=0.000).The application of DAEcc reduced by 50 % the time spent estimating DA compared to the method proposed by Demirjian. Although forensic odontology focuses on the study of the effectiveness of age estimation, its practical use needs to be addressed as well.

Comparing the relative influence of obesity and ancestry on timing of dental development.

INTRODUCTION: The dentition is one of the most accurate features for age estimation in children. However, there is some evidence that timing of dental development varies across populations. Recent research suggests that dental development may also be susceptible to influence by environmental factors, such as obesity. Given that there are also population differences in average body mass, it is possible that this may be a confounding variable that was not accounted for in prior work. We aim to compare the relative association between body mass index (BMI) and ancestry with timing of dental development. METHODS: A retrospective cross-sectional chart review was undertaken using panoramic radiographs of 6- to 12-year-olds (n = 281). Age, ancestry, sex, height, and weight were obtained. Dental development was assessed using the Demirjian method, and chronological age was subtracted from estimated dental age to determine relative timing of dental development (ΔAge). BMI was calculated based upon recorded height/weight within 6 months of time of radiograph. RESULTS: We found no difference in timing of dental development (accelerated/delayed) across ancestry groups (African-American, Euro-American, Hispanic, Asian; P = .15). Overweight/obese subjects had statistically significantly advanced (precocious) dental development (P < .001). Compared to normal-weight subjects, children who were overweight's age were overestimated by 5.76 months (0.48 years) and children with obesity by 5.97 months (0.49 years) on average. CONCLUSIONS: BMI appears to have a greater impact on relative timing of dental development than ancestry in this population. Our results support other findings that obesity results in accelerated growth and development.

Association between dental and skeletal maturation in Scandinavian children born between 2005 and 2010.

OBJECTIVE: The aim of this cross-sectional study was to analyse the association between dental and skeletal maturation in children born between 2005 and 2010. MATERIALS AND METHODS: Dental and skeletal maturation of 117 ethnic Scandinavian children born between 2005 and 2010 (70 girls, 47 boys, mean age 11.48 years) was analysed. Dental maturation (DM) was assessed on orthopantomographs (OPs) by using Demirjian's and Haavikko's methods while skeletal maturation was assessed on hand-wrist radiographs by use of Helm's method. The correlation between skeletal and DM was analysed using Spearman's rho (Rs). Additionally, the most frequent DM stage in relation to the skeletal maturation stage was analysed by logistic regression adjusted for age and sex. RESULTS: The correlation between dental and skeletal maturation was significant for all teeth (Rs = 0.071-0.562; p < .000-p = .035) except for the first incisor and the first molar. Logistic regression analysis showed that when the mandibular and maxillary canines are ¾ mineralized, this is significantly associated with the beginning of the adolescent period before peak height velocity (PHV) (PP2= p < .005-< .05). Likewise, when the mandibular second premolars are ¾ mineralized, this is significantly associated with the maturation stage PP2= or S (PP2= p < .05, S: p < .005-< .05), both of which are before PHV at the beginning of the adolescent period. LIMITATIONS: Limited sample size and the X-rays were taken before orthodontic treatment, which may have introduced a selection bias. CONCLUSIONS: When the root of the canines or second premolars is ¾ mineralized, it may indicate the beginning of the adolescent period with increased skeletal growth intensity.

Differences in third molar development and angulation in class II subdivision malocclusions.

PURPOSE: To assess and compare the developmental stages and angulations of third molars between the class II and class I sides in class II subdivision malocclusions. METHODS: This retrospective study was performed using panoramic x­rays of 38 individuals (mean age: 15.5 years; 24 females, 14 males) with class II subdivision malocclusions, which were further divided into type 1 and 2 subgroups according to midline deviation, and a control group of 42 individuals (mean age: 17.0 years; 30 females, 12 males) with normal occlusion. Third molars were categorized using the developmental stages defined by the Demirjian method. Angles between the third molars and horizontal reference lines and also to the second molars were measured. RESULTS: No difference was found in developmental stages or angulations between the left and right third molars in the control group. In the class II subdivision malocclusion cases, no difference in third molar developmental stages was observed, but the angle between the long axes of the mandibular third and second molars was significantly greater on the class II side. In the type 2 subgroup, developmental stage of the maxillary third molar was more advanced on the class II side. In both subgroups, the angles of the maxillary third molars' long axis to the interorbital plane differed significantly between the two sides. CONCLUSION: Class II subdivision malocclusion may cause differences in third molar development and angulations between the two sides. Orthodontic treatment should be planned considering the third molars in this malocclusion.

Demirjian method and Willems method to study the dental age of adolescents in Shanghai before and after 10 years.

BACKGROUND: The aim of this study was to assess whether the difference in dental ages between 2009 to 2011 and 2021 is affected by environmental factors such as environmental pollution, floating population, and dietary habits. MATERIALS AND METHODS: Demirjian and Willems dental age estimation methods were conducted for a Han population of children aged 8 to 14 in Shanghai, China. A total of 1259 digital panoramic radiographs of children aged 8.00-14.99 were estimated. All digital panoramic radiographs were estimated using the Demirjian and Willems methods. Data collected in 2009 to 2011 and 2021 were statistically analysed by paired t-tests. RESULTS: The results show that the Demirjian method overestimates 2009 to 2011 and underestimates 2021 the children's chronological age. When using the Willems method, the age of males was overestimated and the age of females was underestimated from 2009 to 2011, and it was underestimated for both genders in 2021. CONCLUSIONS: In conclusion, the difference in dental ages between 2009 to 2011 and 2021 was statistically significant. Factors such as environment and dietary habits have been found to be affected by dental development. However, there are disputes among some researchers about the exact factors, so it is suggested to further study the effects of environmental factors on tooth development.

A Comparative Study of the Assessment of the Accuracy of Willems' and Demirjian's Methods in Dental Age Estimation of the Saudi Arabian Population.

BACKGROUND: The notion that "age is just a number" is sometimes expressed colloquially; however, within the domains of medicine and dentistry, age is not only a numerical value. Rather, it has significant importance in several facets of diagnosis and treatment planning. Demonstrating one's identity is necessary not only in the aftermath of natural calamities or mishaps but also in the context of forensic examination of living persons. Age assessment is a very important tool in forensic odontology and plays a vital role in various fields, including medico-legal matters. The primary focus of the current investigation is to assess the precision and correctness of the data and reliability of Demirjian's method (DM) and Willems' method (WM) for age estimation (AE) among the population of Saudi Arabia. METHODOLOGY: This research was conducted on 300 children from Saudi Arabia, aged seven to 13 years, including both males and females. The assessment of orthopantomography images involved the utilization of both Demirjian's and Willems' methodologies for ascertaining dental age. This determined dental age was then meticulously juxtaposed with the chronological age (CA) of every participant. The data collected underwent a comprehensive statistical analysis, which encompassed the application of the paired t-test. RESULTS: By using both methodologies, it was discovered that the estimated age (EA) exhibited higher values in both men and females compared to the CA. Both Willems' and Demirjian's approaches yielded significantly different results in terms of statistical significance (p = 0.000 and p = 0.000, respectively), as shown by the comparison. CONCLUSION: When comparing Willems' technique to Demirjian's method, it was found that the population under research showed somewhat greater accuracy levels for AE. However, it is important to note that the disparity between these two approaches was relatively small. Consequently, it is imperative to emphasize the necessity for additional research involving a larger sample size to establish the validation of a more region-specific AE method tailored specifically for the Saudi Arabian population.

Comparison of Demirjian's comprehensive chart with the London atlas of tooth development in children and adolescents: a pilot study.

Dental age estimation has its application in various subdisciplines of medicine and dentistry. New methods of dental age (DA) estimation are emerging and it is important that we compare different methods to determine which one is more closely related to the chronological age. Demirjian's method is one of the most widely used techniques and has been tested in various ethnic populations globally. In 2016, another approach to DA estimation is the London atlas of human tooth development and eruption. No study has compared Demirjian's comprehensive chart and London atlas method in the Indian population. Hence, in the current study, we estimated DA using Demirjian's comprehensive chart and London atlas method for association with the known chronologic age in children and adolescent population. The study also attempted to determine if sexual dimorphism existed in DA estimated by the two methods. Estimation was performed for both methods on 100 orthopantomogram records (50 males and 50 females, aged 6-16 years) of orthodontic patients. The data were compared and analysed using paired t-tests. There was an overestimation of DA by Demirjian's comprehensive chart on an average of +1.3 years in males and +0.5 years in females, whereas using London atlas, it was +1.4 years in males and +0.5 years in females. The mean of underestimation was -0.6 years in males and -0.8 years in females using Demirjian's comprehensive chart, whereas it was -0.8 years in males and -0.5 years in females. A statistically significant difference (P < 0.0001) was found when mean chronological age (11.6 ± 2.6) years of the participants was compared with DA estimated using either Demirjian's comprehensive chart (12.3 ± 2.8) years or London atlas (11.8 ± 2.9) years. The trends in this pilot study point towards more accuracy of London atlas over Demirjian's method when done using comprehensive chart for estimating DA. In summary, the results of the current pilot study indicates greater accuracy of London atlas method over Demirjian's comprehensive chart method for estimating DA in Indian population. This finding should be validated by conducting similar studies using larger sample, on diverse Indian ethnic populations, for applicability in pedodontic, orthodontic, and forensic domains. Key points: No study has compared Demirjian's comprehensive chart and London atlas method in the Indian population.The dental age estimated by London method was closer to chronological age as compared to Demirjian's method in our study sample.Significant difference was found in chronological age and estimated age using London atlas method in both males and females.

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.

Estimating the 18-Year Threshold with Third Molars Radiographs in the Southern Italy Population: Accuracy and Reproducibility of Demirjian Method.

The estimation of the age of the majority of living subjects is widely required nowadays due to the presence of unidentifiable individuals, without documents and general information, involved in migration or legal procedures. Dental age estimation (DAE) is a valid method for investigating the age of subjects. The aim of this study was to evaluate the accuracy of the Demirjian method in a limited age group (16-24 years) in differentiating between older and younger than 18 years. From an initial sample of 17,594 radiographs, 460 were selected meeting the inclusion criteria. Two dentists provided the age estimate according to the Demirjian method, with a simplified approach based on the development of the third molars. The presence of a developmental stage of H for at least one third molar allowed to establish the major age if the other third molars, inferior or superior, have reached a stage equal or superior to F, with an accuracy of 90.2% and a predictive positive value of 91.6%. Thirty-three patients showed the development of at least one third molar (Stage H) before the age of 18 years while six patients showed the development of all four third molars with root completion (stage H) before the age of 18 years. When all third molars reached stage H an individual was over 18 years old in 97.4% of cases. In presence of one third molar on stage H and a stage equal or superior to F for the other third molars the probability of being of major age was 91.6%.

Accuracy of age estimation with Demirjian and Nolla methods in Eastern Turkish children aged 3-17 years old.

Purpose: Dental age assessment is one of the most reliable methods of chronological age estimation used for criminal, forensic and anthropologic purposes. This study aimed to determine how accurate it was to estimate the chronological age by looking at the dental age measured with the Nolla and Demirjian methods in a Turkish sample, based on the variables of gender and age-group. Materials and methods: A retrospective study was performed on panoramic radiographs of 1587 subjects (774 females and 813 males), aged 3-17 years. The mean dental age according to the Demirjian and Nolla methods were compared to the mean chronological age (CA). Also, the percentage value of prediction of CA was determined by using the both methods. Results: An under-estimation of the chronological age was observed by using Nolla's method (males -0.003, females -0.32, both -0.16) and an over-estimation of the dental age was observed by using Demirjian's method (males 0.61, females 0.75,both 0.68). Conclusion: Nolla's method was more accurate in the CA estimation than Demirjian's method in Eastern Turkish population.

The Application of Artificial-Intelligence-Assisted Dental Age Assessment in Children with Growth Delay.

BACKGROUND: This study aimed to reveal the efficacy of the artificial intelligence (AI)-assisted dental age (DA) assessment in identifying the characteristics of growth delay (GD) in children. METHODS: The panoramic films matching the inclusion criteria were collected for the AI model training to establish the population-based DA standard. Subsequently, the DA of the validation dataset of the healthy children and the images of the GD children were assessed by both the conventional methods and the AI-assisted standards. The efficacy of all the studied modalities was compared by the paired sample t-test. RESULTS: The AI-assisted standards can provide much more accurate chronological age (CA) predictions with mean errors of less than 0.05 years, while the traditional methods presented overestimated results in both genders. For the GD children, the convolutional neural network (CNN) revealed the delayed DA in GD children of both genders, while the machine learning models presented so only in the GD boys. CONCLUSION: The AI-assisted DA assessments help overcome the long-standing populational limitation observed in traditional methods. The image feature extraction of the CNN models provided the best efficacy to reveal the nature of delayed DA in GD children of both genders.

Estimation of dental age using Willems method and comparing it with Demirjian's method in 7-14-year-old children of Uttarakhand.

Age is one of the essential factors, which plays an important role in every aspect of life. Age is estimated on the basis of chronological age, bone age, dental age, mental age and others. This study represents the objective of an ideal age estimation technique to arrive at an age as close to the chronological age as possible. Aim: The aim of this comparative study was to estimate the dental age using Willems method by assessing the developmental stages of left seven permanent mandibular teeth in 7-14 years of age groups with the help of digital orthopantomogram and comparing it with Demirjian's method to conclude which method was more appropriate and better in Indian population of Uttarakhand region. Materials and Methods: A total of 100 digital orthopantomogram films of patients in the age group of 7-14 years was collected over the study period of 3 years from July 2014 to January 2017 and was equally distributed by convenience sampling. Group I included 50 boys and Group II included 50 girls aging 7-14 years of age (as permanent 7 teeth in the 3rd quadrant were to be assessed and scored according to the Demirjian's table, the 7-14 age group was selected). The date of birth of the subject was documented against their allocated identification number. Dental age according to Willem's method was calculated using Willem's table and Demirjian and Goldstein's table scores were used to calculate dental age by Demirjian's method. Results: Willems method was more accurate and better than Demirjian's method as it showed less mean percentage error, i.e., 0.34% as compared to 15.94% obtained from Demirjian's method in Group II and 2.19% as compared to 8.05% obtained from Demirjian's method in Group I. Conclusion: The results suggested that Willems method of dental age estimation was an effective method of age estimation and was better and more accurate than Demirjian's method.

Validity of the Demirjian Method for Dental Age Estimation in Romanian Children.

Dental age assessment is useful in various medical fields. The Demirjian method for dental age estimation is one of the most widely used in the field of pediatric dentistry. The aim of this study was to verify the accuracy of the Demirjian method in determining age in a sample of girls and boys from Oradea, Romania. This retrospective and radiographic study was based on the evaluation of 1006 panoramic radiographs, belonging to 1006 patients (431 boys and 575 girls) with ages between 3 and 13.9 years from the city of Oradea, Romania. They were collected from three private dental practices from Oradea and were analyzed between 1 September 2021, and 10 November 2021. The patients were distributed into 11 age groups, each group spanning over one year (e.g., 3−3.9, 4−4.9). For the assessment of dental age, the Demirjian method was used, which is based on the evaluation of the developmental stages of the lower left permanent teeth. The mean chronological age of the patients was 9.496 ± 2.218 years, and the mean dental age was 10.934 ± 2.585 years. The overestimation of dental age was obtained in all age groups for the entire sample. As such, dental age was higher than chronological age, with values varying from 0.34 years in the 3−3.9 years age group to 1.7 years in the 10−10.9 years age group. In girls, dental age was higher than chronological age, with values varying from 0.46 years in the 3−3.9 years age group to 1.73 years in the 11−11.9 years age group, while in boys, the values varied from 0.15 years in the first age group to 2.02 years in the 10−10.9 years age group. The comparison of the differences between chronological age and dental age according to the gender of the patients revealed that the distribution of the differences was nonparametric in both groups according to the Shapiro−Wilk test (p < 0.05). The differences between the groups were not significant according to the Mann−Whitney U test. However, larger differences were identified for boys (1.46 years) than girls (1.417 years), with an overestimation of the dental age. The Demirjian method overestimated the age of the children included in the investigated sample, with different values for the different age groups investigated, and requires adaptations.

With or without human interference for precise age estimation based on machine learning?

Age estimation can aid in forensic medicine applications, diagnosis, and treatment planning for orthodontics and pediatrics. Existing dental age estimation methods rely heavily on specialized knowledge and are highly subjective, wasting time, and energy, which can be perfectly solved by machine learning techniques. As the key factor affecting the performance of machine learning models, there are usually two methods for feature extraction: human interference and autonomous extraction without human interference. However, previous studies have rarely applied these two methods for feature extraction in the same image analysis task. Herein, we present two types of convolutional neural networks (CNNs) for dental age estimation. One is an automated dental stage evaluation model (ADSE model) based on specified manually defined features, and the other is an automated end-to-end dental age estimation model (ADAE model), which autonomously extracts potential features for dental age estimation. Although the mean absolute error (MAE) of the ADSE model for stage classification is 0.17 stages, its accuracy in dental age estimation is unsatisfactory, with the MAE (1.63 years) being only 0.04 years lower than the manual dental age estimation method (MDAE model). However, the MAE of the ADAE model is 0.83 years, being reduced by half that of the MDAE model. The results show that fully automated feature extraction in a deep learning model without human interference performs better in dental age estimation, prominently increasing the accuracy and objectivity. This indicates that without human interference, machine learning may perform better in the application of medical imaging.