Insights into dental age estimation: introducing multiple regression data from a Black South African population on modified gustafson's criteria.

Dental Age Estimation (DAE) is an effective instrument of the rule of law for verifying dubious age claims in living individuals. Once tooth development is complete, only degenerative dental characteristics can be used for this purpose. The influence of ethnicity on these degenerative dental characteristics has not been clarified.Degenerative changes were examined using modified Gustafson's criteria including secondary dentin formation, cementum apposition, periodontal recession and attrition using the Olze et al. (2012) staging scales. Orthopantomograms of 1882 black South Africans, consisting of 934 females and 948 males, from 12.00 to 40.96 years of chronological age were utilized. Two independent examiners performed the evaluations, with one of the two evaluating all radiographs twice.The relationship between individual characteristics and chronological age was analyzed using multiple regression analysis with chronological age as the dependent variable. The resulting R2 values ranged from 0.22 to 0.35, and the standard error of estimate were between 6.6 and 7.3 years. The correlation with age was consistently lower for females compared to males. The characteristic of cementum apposition emerged as critical in this population, due to a particularly low correlation with age and observer agreements partly in the "slight" range. The formula's values for the correlation with age were in general below the literature values for other populations. Overall, the limited precision of the age estimation by the formulae presented, especially for females, must be emphasized. The question of whether ethnicity per se exerts an influence on the characteristics in question, or whether the different socio-economic status, which encompasses factors such as nutrition and healthcare, is the determining factor, needs to be assessed in future studies.

Evaluation of tooth development stages with deep learning-based artificial intelligence algorithm.

BACKGROUND: This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients. METHODS: The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model. RESULTS: Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively. CONCLUSIONS: In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options.

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.

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.

Estimating dental age of New Zealand juveniles and subadults using Demirjian's method.

Reference data for dental age estimate is sparse in New Zealand (NZ), with only two contemporary studies. Te Moananui et al. (J For Sci. 53(2), 2008) presented modified Demirjian percentile curves to estimate dental age of Pasifika, Maori, and European males and females (n = 1383), while Timmins et al. (Forensic Sci Med Pathol. 8:101-8, 2012) found the Demirjian method (1973) was valid for a smaller sample (n = 200) of unknown ancestry. The study presented here sought to validate the Demirjian and the Te Moananui methods for a sample of the NZ population of unknown ancestry and a subgroup of known ancestry i.e., Pasifika, Maori and European, for males and females. The Demirjian method (1976) was applied to the current study's sample consisting of 3523 individuals aged 4 to < 20 years. The seven left mandibular teeth (third molar excluded) and tooth scores were summed for each individual, with the Te Moananui methods applied to this subgroup. The results revealed these methods to be less than ideal for estimating dental age of the NZ sample, for both males and females. The probit regression form of Transition Analysis (TA) was employed to calculate the mean age entering each tooth stage, for the seven teeth, to reduce age mimicry that is commonly associated with traditional regression analysis. TA results revealed Pasifika and Maori individuals to be more advanced than Caucasian individuals. The sex groups were also compared to the mean ages presented by Demirjian and Levesque with mixed results (J Dent Res. 59(7):1110-22, 1980), highlighting the need for more research in this area.

Dental age estimation: development and testing of a reference data set for Qatari children, adolescents, and young adults aged between 5 and 25 years.

The purpose of this study is to establish and test a reference data set of dental development of Qatari subjects aged between 5 and 25 years. Radiographs of individuals aged between 5 and 25 years were re-used to establish a reference data set (RDS). A scheme comprising 8 tooth development stages (TDS) was used to assess all the teeth on the left side of the maxilla and mandible. The accuracy of dental age estimation (DAE) was tested with a separate sample of radiographs - the validation sample (VS) comprised 50 females and 50 males of known chronological age (CA). Dental panoramic tomographs (DPT) of 1,597 Qataris were assessed. The summary data for the individual TDS comprising the number (n-tds), mean ( x ¯ -tds), standard deviation (sd-tds), 0th%-ile (the minimum), 25th%-ile, 50th%-ile (the median), 75th%-ile, and 100th%-ile (the maximum) were used to estimate the age of the VS subjects using the simple average method (SAM). There is a significant difference in dental age of 4.8 months in the female group when compared to the CA. The difference in the male group is 4.5 months. This shows similar differences to assessments of other ancestral or ethnic groups.

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.

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.

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.

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.

The Cameriere, Haavikko, Demirjian, and Willems methods for the assessment of dental age in Croatian children.

AIM: This study aimed to evaluate the accuracy and precision of the Cameriere European formula, Demirjian, Haavikko, and Willems methods for estimating dental age in a sample of children with permanent dentition in Croatia. MATERIAL AND METHODS: The study consisted of a sample of 1576 panoramic radiographs; a sub-sample of 84 OPGs, in which all first seven mandibular teeth were maturated, was excluded from the study. A final sample of 1492 (704 males and 788 females) aged 6.0 to 13.9 years was evaluated. Seven mandibular teeth from the left side of the mandible were analyzed, and dental age (DA) was determined by the Cameriere European formula, Demirjian method from 1976, Haavikko, and Willems methods and compared to chronological age (CA). In addition, the mean age difference (DA-CA), the mean absolute error (MAE) between dental and chronological age, the percentage of the individuals of dental age within ± 0.25 to ± 2 years of chronological age, and intra-observer and inter-observer statistics were calculated. RESULTS: The Cameriere European formula estimated the best dental age compared to the chronological age; the mean underestimation was - 0.4 years for both sexes, Haavikko underestimated by - 0.17 years, while Demirjian and Willems overestimated by 1.02 years and 0.48 years, respectively. The most significant difference showed the Demirjian method in 11-year-old and 12-year-old females. The MAE were 0.50 years, 1.01 years, 0.61 years, and 0.78 years in males and 0.51 years, 1.18 years, 0.61 years, and 0.70 years in females for the Cameriere European formula, Haavikko, Willems, and Demirjian methods, respectively. Furthermore, the Cameriere European formula showed the highest proportions of individuals with DA within ± 0.5 year difference of the CA, 61.5% in males and 59.6% in females. In addition, the Cameriere method showed the best intra-observer and inter-observer agreements. CONCLUSIONS: Although the Demirjian method was used previously in Croatian children for legal, medical, and clinical purposes, the Cameriere European formula, Haavikko, and Willems were more accurate in the tested sample. According to our findings, the Cameriere European formula showed the best accuracy and precision in dental age assessment in Croatian children following Haavikko, and we recommend it as the method of the first choice in forensic and clinical analyses.

A new model of classification of third molars development and its correlation with chronological age in a Brazilian subpopulation.

The present study correlated the mineralization of third molars to chronological age using a modified classification based on Demirjian's stages in a Brazilian subpopulation and compared with the original classification. A total of 1082 patients with age ranging from 6 to 26 years were included in the sample, with at least one third molar on panoramic radiographs. The third molars were classified according to the original Demirjian classification (8 stages) and a new model based on the Demirjian method, where the original stages were grouped into four stages: AB-enamel mineralization; CD-crown dentin mineralization; EFG-root formation; and H-complete development. Statistical analyses were performed by Kruskal-Wallis/Dunn tests (α = 0.05) and the multinomial logistic regression model. Data were analyzed according to percentiles for the probability of an individual being over 18 years old. The mean ages of the stages in both classifications did not present a significant difference between superior and inferior arches (p < 0.05). The differences in mean ages between all the stages of mineralization were statistically significant (p < 0.001) only for the 4-stage classification. Males attained root formation and complete formation earlier than females (p < 0.05) in the 4-stage classification. The modified classification system showed dependence between chronological age and mineralization stages of third molars, simplifying the age estimation process. At stage H, females present a 95.7% chance of being over 18, while for males, this probability is 89.6%. This modified classification system simplifies the dental age estimation process based on third molars and can be used as a reference for future studies.

Comparison of different machine learning approaches to predict dental age using Demirjian's staging approach.

CONTEXT: Dental age, one of the indicators of biological age, is inferred by radiological methods. Two of the most commonly used methods are using Demirjian's radiographic stages of permanent teeth excluding the third molar (Demirjian's and Willems' method). The major drawbacks of these methods are that they are based on population-specific conversion tables and may tend to over- or underestimate dental age in other populations. Machine learning (ML) methods make it possible to create complex data schemas more simply while keeping the same annotation system. The objectives of this study are to compare (1) the capacity of ten machine learning algorithms to predict dental age in children using the seven left permanent mandibular teeth compared to reference methods and (2) the capacity of ten machine learning algorithms to predict dental age from childhood to young adulthood using the seven left permanent mandibular teeth and the four third molars. METHODS: Using a large radiological database of 3605 orthopantomograms (1734 females and 1871 males) of healthy French patients aged between 2 and 24 years, seven left permanent mandibular teeth and the 4 third molars were assessed using Demirjian's stages. Dental age estimation was then performed using Demirjian's reference method and various ML regression methods. Two analyses were performed: with the 7 left mandibular teeth without third molars for the under 16 age group and with the third molars for the entire study population. The different methods were compared using mean error, mean absolute error, root mean square error as metrics, and the Bland-Altman graph. RESULTS: All ML methods had a mean absolute error (MAE) under 0.811 years. With Demirjian's and Willems' methods, the MAE was 1.107 and 0.927 years, respectively. Except for the Bayesian ridge regression that gives poorer accuracy, there was no statistical difference between all ML tested. CONCLUSION: Compared to the two reference methods, all the ML methods based on the maturation stages defined by Demirjian were more accurate in estimating dental age. These results support the use of ML algorithms instead of using standard population tables.

Dental maturation in a Chinese sample using Demirjian method.

BACKGROUND: Dental calcification information is relevant for clinical, archaeological, and forensic applications. However, dental maturity measurements in current cohorts of Chinese children are insufficient. AIM: This study aimed to establish the mandibular dental maturity table and determine the accuracy of dental age estimation using the Demirjian method in a Chinese sample. SUBJECTS AND METHODS: Permanent mandibular teeth, excluding the third molar, in 2091 panoramic radiographs of 1008 males and 1083 females aged 3-15 were graded, and dental age was estimated according to Demirjian's criteria. Age-of-attainment was calculated with probit regression for each stage by sex, and sex differences were analysed using the Mann-Whitney U test. Dental age was compared with chronological age using paired t-tests. RESULTS: Although females showed earlier age-of-attainment than males, significant sex differences were only found in stages D-G (p < 0.05). The Demirjian method overestimated the dental age in both males (0.68 ± 1.05 years) and females (0.59 ± 0.97 years). CONCLUSIONS: The relationship between mineralisation stage and chronological age in this study can be applied as a reference for mandibular dental maturity of Chinese children and adolescents. The Demirjian method overestimated the chronological ages of this sample.

A modified dental age assessment method for 5- to 16-year-old eastern Chinese children.

OBJECTIVES: Age estimation is widely applied in the field of orthodontics, pediatric dentistry, and forensic science. Dental age estimation by the radiological method is frequently used because of its convenience and noninvasiveness. However, there are not enough suitable methods for eastern Chinese children. This study aimed to establish a modified formula for eastern Chinese children according to the Demirjian method and then compared the accuracy of the modified method with the Demirjian method and Willems method. MATERIALS AND METHODS: A total of 2367 dental panoramic radiographs from individuals aged 5-16 years of eastern China were collected as samples. Age estimation was conducted using the Demirjian and Willems methods. The polynomial curve fitting method was used to modify the Demirjian method to improve its application to the eastern Chinese children. The paired t test and accuracy ratio were used to compare the applicability of the modified methods with two commonly used methods. RESULTS: The mean chronological age (CA) of the subjects was 11.20 ± 3.29 years for boys and 10.99 ± 3.12 years for girls. The mean difference values between the CA and dental age (DA) (CA-DA) using the Demirjian and Willems methods were 0.73 and 0.7 for boys, respectively, and both 0.79 for girls. The modified method using the polynomial curve fitting presented a smaller underestimation compared with CA for both boys (0.04 years) and girls (0.09 years), which showed a high suitability to Chinese children to some extent. CONCLUSIONS: The Willems method was more accurate in estimating DA compared with the Demirjian method. However, the modified method was more accurate than the two methods; therefore, it can be used in eastern Chinese children. CLINICAL RELEVANCE: It was thought to be a non-invasive, convenient, and efficient method to connect DA and CA. By estimating dental age, pediatrist, and orthodontists can better understand the development of permanent teeth and provide a more accurate orthodontic treatment time and treatment plan to children patients.

Dental age in children with impacted maxillary canines.

OBJECTIVE: The aim of this study was to evaluate if delayed dental development is a cause of postponed care for patients with impacted maxillary canine (IMC). MATERIALS AND METHODS: This case-control study was based on 403,355 children and adolescents in Region Västra Götaland, Sweden. The subjects, who were in the age range of 9-16 years during the period of 2011-2013, underwent surgical exposure or removal of a maxillary canine. Demirjian's dental age assessment was carried out on panoramic radiographs. RESULTS: In total, 1028 patients, 514 with IMC and 514 age- and gender-matched controls, were enrolled. The patients with IMC exhibited a dental development delay of 0.2 years compared to the control group. In the impaction sub-groups, the female patients, patients in the chronological age group of 12-13 years, and patients with palatally positioned IMC had a significantly lower dental age than their paired-control subjects. CONCLUSIONS: Overall, the difference in dental age between patients with or without IMC is significant but small, and as such is likely of minor clinical relevance. Therefore, the timing of preventive care and treatment for patients with IMC should be the same as that for patients with normally erupting canines.

Correlations among chronological age, cervical vertebral maturation index, and Demirjian developmental stage of the maxillary and mandibular canines and second molars.

PURPOSE: Estimation of growth spurt from chronological age or dental development is of clinical interest to orthodontists. Since results in this regard are highly controversial and limited, this study was conducted to investigate associations among chronological age, skeletal development (cervical vertebral maturity [CVM]), and dental calcification (Demirjian) in girls and boys, independently. METHODS: Panoramic radiographs and lateral cephalographs of 112 boys and 112 girls were evaluated. Demirjian stages of dental development of the bimaxillary canines and second molars were determined. CVM stages of skeletal growth were as well estimated. Correlations among these were assessed. Differences between sexes and between maxilla/mandible arches were assessed. Cutoff points in Demirjian and chronological age reflecting skeletal growth spurt were found using receiver operator characteristic curve (α = 0.05, ß = 0.9 separately for girls and boys). RESULTS AND CONCLUSIONS: Sex dimorphism existed both in CVM index and in Demirjian indexes. Compared to dental development and calcification, chronological age was the best predictor of skeletal growth and maturation. In estimating chronological age by radiography means, in girls, Demirjian method was better than CVM. In boys, Demirjian was better than CVM in the case of the molars but not canines. The cutoff points estimated for chronological age and dental calcification that can reflect skeletal growth spurt (between CS-3-and-CS-4) were as follows: in boys, age of 12 years; in girls, age between 11 and 12 years; the upper and lower canines: between G and H; the maxillary and mandibular second molars: between F and G; in the case of all teeth: between F and G.

Accuracy assessment of dental age estimation with the Willems, Demirjian and Nolla methods in Spanish children: Comparative cross-sectional study.

BACKGROUND: The objective of this study was to evaluate and compare the validity and accuracy of the Willems, Demirjian and Nolla methods in predicting chronological age in a Spanish ethnicity population. METHODS: A sample of 604 orthopantomographs of Spanish children aged 4 to 13 years was evaluated by two independent evaluators. Descriptive statistics were applied to calculate the chronological age and dental age, presenting the mean and standard deviation. The difference between dental age and chronological age was calculated for each method. A positive result indicated an overestimation and a negative figure indicated an underestimation. The Wilcoxon test for paired data and Spearman's correlation coefficient were applied by age groups and sex to compare the chronological age and dental age of each method (that of Willems, Demirjian and Nolla). Statistical tests were performed at a 95% confidence level. RESULTS: The interexaminer agreement was 0.98 (p = 0.00), and the intraexaminer agreement was 0.99 (p = 0.00). The Willems method significantly overestimated the age of boys (0.35 years (0.93)) and girls (0.17 years (0.88)). The Demirjian method significantly overestimated the age of boys (0.68 years (0.95)) and girls (0.73 years (0.94)). The Nolla method significantly underestimated age in boys (0.44 years (0.93)) and girls (0.82 years (0.98)). CONCLUSIONS: In the Spanish population, the use of the Demirjian method for legal and medical purposes is frequent. This study reveals that the Willems method is more appropriate due to its greater precision in estimating dental age.

Comparison of three methods to estimate dental age in children.

OBJECTIVES: In 1973, Demirjian and colleagues introduced a staging for tooth mineralization. Staging of the teeth of the left mandible generates a dental maturity score and an estimate of dental age. An update of this method was published in 2001 by Willems and colleagues. The London Atlas (2008) is another method used to assess dental age. This study compared the precision of these three methods for forensic age estimation. MATERIALS AND METHODS: A total of 500 panoramic images of 251 boys and 249 girls aged 6-16 years had dental maturity evaluated by the same observer using the London Atlas and the methods of Demirjian and Willems. Individuals with syndromes potentially affecting dental maturation were excluded. The three methods were compared with respect to their precision in estimating chronological age. RESULTS: Age estimates using Willems' method were significantly more precise than those obtained using Demirjian's method, while estimates obtained using the London Atlas method were comparable with both the others. The highest precision was obtained using the average of age estimates from Willems' method and the London Atlas. CONCLUSIONS: To estimate age in individuals suspected of being below 16 years of age, we suggest first applying the London Atlas then computing age from the Demirjian stages of particular teeth using Willems' method, and finally using the average age estimate of both methods. CLINICAL RELEVANCE: Combining the London Atlas and Willems' scoring method provides more precise estimates of dental age than the current practice of applying a single preferred method.