Dental age and tooth development in children with molar-incisor hypomineralization: A case-control study

Comparison of different dental age estimation methods with deep learning: Willems, Cameriere-European, London Atlas

This study aimed to compare dental age estimates using Willems, Cameriere-Europe, London Atlas, and deep learning methods on panoramic radiographs of Turkish children. The dental ages of 1169 children (613 girls, 556 boys) who agreed to participate in the study were determined by 4 different methods. The Convolutional Neural Network models examined were implemented in the TensorFlow library. Simple correlations and intraclass correlations between children's chronological ages and dental age estimates were calculated. Goodness-of-fit criteria were calculated based on the errors in dental age estimates and the smallest possible values for the Akaike Information Criterion, the Bayesian-Schwarz Criterion, the Root Mean Squared Error, and the coefficient of determination value. Simple correlations were observed between dental age and chronological ages in all four methods (p < 0.001). However, there was a statistically significant difference between the average dental age estimates of methods other than the London Atlas for boys (p = 0.179) and the four methods for girls (p < 0.001). The intra-class correlation between chronological age and methods was examined, and almost perfect agreement was observed in all methods. Moreover, the predictions of all methods were similar to each other in each gender and overall (Intraclass correlation [ICCW] = 0.92, ICCCE=0.94, ICCLA=0.95, ICCDL=0.89 for all children). The London Atlas is only suitable for boys in predicting the age of Turkish children, Willems, Cameriere-Europe formulas, and deep learning methods need revision.

Is there an association between molar incisor hypomineralization and developmental dental anomalies? A case-control study

BACKGROUND

The aim of this study was to determine whether there is any association between molar incisor hypomineralization and developmental dental anomalies.

METHODS

Two pediatric dentists evaluated panoramic radiographs of 429 children aged 8-14 years with molar incisor hypomineralization (study group) and 437 children without molar incisor hypomineralization (control group) in terms of developmental dental anomalies. Twelve different developmental dental anomalies were categorized into four types: size (microdontia, macrodontia); position (ectopic eruption of maxillary permanent first molars, infraocclusion of primary molars); shape (fusion, gemination, dilaceration, taurodontism, peg-shaped maxillary lateral incisors); and number (hypodontia, oligodontia, hyperdontia) anomalies.

RESULTS

No significant difference was observed in the frequencies of developmental dental anomalies between the study and control groups in total, females, and males (p > 0.05). A statistically significant difference was found between the distribution of developmental size, position, shape, and number anomalies between the study and control groups (p = 0.024). The most common anomaly in both groups was hypodontia (6.3% and 5.9%, respectively). There was a significant difference between the study and control groups in terms of subtypes of shape anomaly in all children and females (p = 0.045 and p = 0.05, respectively).

CONCLUSIONS

While a significant difference was observed between the distributions of types of developmental dental anomalies between individuals with and without molar incisor hypomineralization, there was no difference in terms of the frequency of developmental dental anomalies.

Assessment of the oral health status of children with chronic kidney disease

BACKGROUND

There are various oral symptoms related to the disease and its management in individuals with chronic kidney disease (CKD). The aim of the study was to investigate the oral health status of children with different stages of CKD, kidney transplant recipients (KTR), and healthy children.

METHODS

A total of seventy-one children diagnosed with CKD and fifty-two healthy children were included in the study. Each patient was examined for dental caries by the decayed-missing-filled-teeth (DMFT/dmft) index and the International Caries Detection and Assessment System (ICDAS-II), developmental defects of enamel (DDE) by the DDE index, and oral hygiene by the debris (DI), calculus (CI), and simplified oral hygiene (OHI-S) indices.

RESULTS

The median number of DMFT/dmft was 1.00 (interquartile range (IQR):1.00-4.00) in children with stage 1-3 CKD, 0.00 (IQR: 0.00-2.50) in stage 4-5 children, 0.00 (IQR: 1.00-3.00) in KTR, and 8.00 (IQR: 1.00-13.00) in healthy children. According to ICDAS-II categories, the percentage of children with severe caries was 53.8% in healthy children, while it was 44.4% in KTR, 25.9% in stage 1-3, and 11.4% in stage 4-5 children. While the percentage of children with DDE was 88.8% in KTR, 80% in stage 4-5, and 66.7% in stage 1-3 children, this rate was 44.2% in healthy children. The highest mean OHI-S score was observed in stage 4-5 children (2.10 ± 1.08), followed by KTR (1.46 ± 1.19), stage 1-3 (1.27 ± 0.61), and healthy children (0.45 ± 0.44), respectively.

CONCLUSIONS

Compared to healthy children, children with CKD had more debris accumulation, calculus formation, and more DDE but a lower severity of dental caries. A higher resolution version of the Graphical abstract is available as Supplementary information.

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

BACKGROUND

Numerous 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.

AIM

This 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.

METHODS

A 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.

RESULTS

Totally, 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).

CONCLUSION

The 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.

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.

Dental age estimation with two different methods in paediatric patients with hypothyroidism

OBJECTIVE

This case-control study aimed to evaluate dental age estimation in children with hypothyroidism using two different methods in order to contribute to the literature about the most accurate method.

DESIGN

In the study, panoramic radiographs of 80 patients with hypothyroidism aged 5-13 years and 80 randomly selected age and sex-matched healthy patients were examined retrospectively. The Cameriere's and the Nolla's methods were used for the dental age estimation. Dental ages and their differences with chronological ages were compared according to group and sex.

RESULTS

No statistically significant difference in estimated dental age was found between the hypothyroidism and control groups using both methods (p > 0.05). Among all the males and females included in this study, the dental ages of the females calculated by Nolla's method were significantly lower than those of the males according to the chronological ages (p < 0.05). For females, in both the hypothyroidism (p < 0.001) and control (p = 0.001) groups, the dental age estimated by Cameriere's method was significantly closer to the chronological age compared to Nolla's method, although there was no statistically significant difference for males.

CONCLUSION

Although the dental age of children with hypothyroidism showed similar results to that of healthy children, it is concluded that Cameriere's method led to more accurate results than Nolla's method in females for both healthy patients and patients with hypothyroidism.