Dataset Information

Assessment of dental age estimation methods applied to Brazilian children: a systematic review and meta-analysis.

ABSTRACT:

Objectives

To review the scientific literature of studies on dental age estimation methods applied to Brazilian children.

Methods

A systematic literature review was designed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and registered in PROSPERO (CRD42020136170). Six scientific databases were used as primary search sources (PubMed, Scopus, LILACS, SciELO, Embase and Web of Science) and two databases (Open Grey and Open Thesis) were searched to partially select the "grey literature." Only cross-sectional studies were included. The risk of bias was assessed by means of Joanna Briggs Institute Critical Appraisal Tools for Systematic Reviews. The standardized mean difference (SMD) between the estimated dental and chronological ages was meta-analysed via random effects model.

Results

The search resulted in 2,527 studies, from which 13 met the eligibility criteria. Out of the eligible studies, 76.92% had low risk of bias and high methodological quality. Ten studies provided proper information to be included in the meta-analysis.The methods and their SMD between estimated and chronological ages were: Willems'=0.05, Lilequist and Lundberg's = -0.11, Nolla's = 0.22, Mornstad's = 0.27, Cameriere's = -0.31, Demirjian's = 0.74 and Haavikko's = -0.87.

Conclusion

Although originally trained in populations worldwide, most of the international methods for radiographical dental age estimation had optimal performance in Brazilian children.

SUBMITTER: Franco A

PROVIDER: S-EPMC7860950 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Assessment of dental age estimation methods applied to Brazilian children: a systematic review and meta-analysis.

Franco Ademir A de Oliveira Murilo Navarro MN Campos Vidigal Maria Tereza MT Blumenberg Cauane C Pinheiro Adriele Alves AA Paranhos Luiz Renato LR

Dento maxillo facial radiology 20200612 2

<h4>Objectives</h4>To review the scientific literature of studies on dental age estimation methods applied to Brazilian children.<h4>Methods</h4>A systematic literature review was designed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and registered in PROSPERO (CRD42020136170). Six scientific databases were used as primary search sources (PubMed, Scopus, LILACS, SciELO, Embase and Web of Science) and two databases (Open Grey and Open The ...[more]

PMID: 32479117

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Project description:Background:Forensic estimation of chronological age has played an increasingly important role as part of cadaver identification, and also in living individuals due to the phenomenon of immigration and sexual abuse of undocumented trafficked children. Objective:This research aimed to validate the already used dental mineralization and eruption atlas in normal children and adolescent population in estimating chronological age in a young population, particularly those with special needs, majority of them with systemic diseases. Participants and Setting:A sample of 163 orthopantomograms from two independent medical institutions was collected from 133 patients aged between 4 and 23 years. Method:The orthopantomograms were divided into two groups, 95 from patients with systemic pathologies that have repercussions on dental development and 68 with systemic pathologies without dental repercussions. Dental ages were estimated by the London Atlas using the left side and then independently the right side of the maxilla. The intraobserver and interobserver agreements were evaluated. The difference between dental age estimates and the chronological age and its absolute value were calculated and analyzed. Results:Statistically significant differences were found between estimates and chronological age, revealing a general prevalence for underestimation; except for those under the age of 12. Nevertheless, the underestimation in individuals under the age of 16 was not significant (with an average of less than one month), while the underestimation was significant for persons who were at least 16 years old (with an average over 26 months). Furthermore, for those persons with systemic diseases with dental repercussions a greater error in underestimation was obtained, which indicates that the midpoint values should be reassessed in persons with Down's syndrome, chromosomal alterations, syndromes and central nervous system disorders. Conclusions:This atlas can be potentially used as a tool for age estimation in a population with special needs and, also, in a population with systemic diseases, but we suggest further studies with larger international samples to create adequate atlases for all the required scenarios, mainly, diagrams for people with systemic diseases who are over the age of 16.

Project description:BackgroundRecently, the dental age estimation method developed by Cameriere has been widely recognized and accepted. Although machine learning (ML) methods can improve the accuracy of dental age estimation, no machine learning research exists on the use of the Cameriere dental age estimation method, making this research innovative and meaningful.AimThe purpose of this research is to use 7 lower left permanent teeth and three models [random forest (RF), support vector machine (SVM), and linear regression (LR)] based on the Cameriere method to predict children's dental age, and compare with the Cameriere age estimation.Subjects and methodsThis was a retrospective study that collected and analyzed orthopantomograms of 748 children (356 females and 392 males) aged 5-13 years. Data were randomly divided into training and test datasets in an 80-20% proportion for the ML algorithms. The procedure, starting with randomly creating new training and test datasets, was repeated 20 times. 7 permanent developing teeth on the left mandible (except wisdom teeth) were recorded using the Cameriere method. Then, the traditional Cameriere formula and three models (RF, SVM, and LR) were used to estimate the dental age. The age prediction accuracy was measured by five indicators: the coefficient of determination (R2), mean error (ME), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE).ResultsThe research showed that the ML models have better accuracy than the traditional Cameriere formula. The ME, MAE, MSE, and RMSE values of the SVM model (0.004, 0.489, 0.392, and 0.625, respectively) and the RF model (- 0.004, 0.495, 0.389, and 0.623, respectively) were lower with the highest accuracy. In contrast, the ME, MAE, MSE and RMSE of the European Cameriere formula were 0.592, 0.846, 0.755, and 0.869, respectively, and those of the Chinese Cameriere formula were 0.748, 0.812, 0.890 and 0.943, respectively.ConclusionsCompared to the Cameriere formula, ML methods based on the Cameriere's maturation stages were more accurate in estimating dental age. These results support the use of ML algorithms instead of the traditional Cameriere formula.

Dataset Information

Assessment of dental age estimation methods applied to Brazilian children: a systematic review and meta-analysis.

Objectives

Methods

Results

Conclusion

Publications

Assessment of dental age estimation methods applied to Brazilian children: a systematic review and meta-analysis.

Similar Datasets

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