Dental age assessment on panoramic radiographs: Comparison between two generations of young Finnish subjects

A systematic overview of dental methods for age assessment in living individuals: from traditional to artificial intelligence-based approaches

Dental radiographies have been used for many decades for estimating the chronological age, with a view to forensic identification, migration flow control, or assessment of dental development, among others. This study aims to analyse the current application of chronological age estimation methods from dental X-ray images in the last 6 years, involving a search for works in the Scopus and PubMed databases. Exclusion criteria were applied to discard off-topic studies and experiments which are not compliant with a minimum quality standard. The studies were grouped according to the applied methodology, the estimation target, and the age cohort used to evaluate the estimation performance. A set of performance metrics was used to ensure good comparability between the different proposed methodologies. A total of 613 unique studies were retrieved, of which 286 were selected according to the inclusion criteria. Notable tendencies to overestimation and underestimation were observed in some manual approaches for numeric age estimation, being especially notable in the case of Demirjian (overestimation) and Cameriere (underestimation). On the other hand, the automatic approaches based on deep learning techniques are scarcer, with only 17 studies published in this regard, but they showed a more balanced behaviour, with no tendency to overestimation or underestimation. From the analysis of the results, it can be concluded that traditional methods have been evaluated in a wide variety of population samples, ensuring good applicability in different ethnicities. On the other hand, fully automated methods were a turning point in terms of performance, cost, and adaptability to new populations.

Assessing Children's Dental Age with Panoramic Radiographs

(1) Background: The aim of the study was to assess the dental age of the subjects and compare it to their chronological age; to assess the dominant tooth for evaluation of dental age; and to investigate possible individual differences between the left and right side of the dental arch. (2) Methods: This study involved evaluating panoramic radiographs of patients aged 7-13 years. A separate assessment was performed for each tooth according to the degree of germ development. Each subject's dental age was estimated. (3) Results: The study involved evaluating 349 panoramic radiographs. No difference was found between stages of tooth development on the right side and left side. Correlation between the stages of tooth development and the chronological age was found to be highest in the second permanent molar teeth. The age can be predicted with the estimated age of tooth #37 and the gender of the patient. (4) Conclusions: The chronological age of children aged 7-13 may be estimated based on a modified seven-stage London Atlas of tooth development, where the most accurate landmark(s) of use are second molars.

Efficacy of machine learning assisted dental age assessment in local population

INTRODUCTION

Although the dental age assessment is commonly applied in forensic and maturity evaluation, the long-standing dilemma from population differences has limited its application.

OBJECTIVES

This study aimed to verify the efficacy of the machine learning (ML) to build up the dental age standard of a local population.

METHODS

We retrospectively studied 2052 panoramic films retrieved from healthy Taiwanese children aged 2.6-17.7 years with comparable sizes in each age-group. The recently reported Han population-based standard (H method) served as the control condition. To develop and validate ML models, random divisions of the sample in an 80%-20% ratio repeated 20 times. The model performances were compared with the H method, Demirjian's method, and Willems's method.

RESULTS

The ML-assisted models provided more accurate age prediction than those non-ML-assisted methods. The range of errors was effectively reduced to less than one per year in the ML models. Furthermore, the consistent agreements among the age groups from preschool to adolescence were reported for the first time. The Gaussian process regression was the best ML model; of the non-ML modalities, the H method was the most efficacious, followed by the Demirjian's method and Willems's methods.

CONCLUSION

The ML-assisted dental age assessment is helpful to provide customized standards to a local population with more accurate estimations in preschool and adolescent age groups than do studied conventional methods. In addition, the earlier complete tooth developments were also observed in present study. To construct more reliable dental maturity models in the future, additional environment-related factors should be taken into account.

Accuracy of the Demirjian's method for assessing the age in children, from 1973 to 2020. A meta-analysis

One of the most common ways to assess the age in subadults using dental records is the Demirjian method. As the number of the studies using this method increased significantly in the recent years, and as their results were often conflicting, we investigated the accuracy of the method. We performed a systematic review and meta-analysis of observational studies obtained from Pubmed, using a random-effects model with the DerSimonian-Laird estimator, and raw mean difference for effect size measure. Prediction intervals (at 95%) were used to assess the presence of significant statistical differences between chronological and dental age. Our meta-analysis showed that Demirijan's method overestimated dental age by 0.48 years in girls and 0.51 in boys. Depending on the location and sex, in girls the smallest average overestimation was found in Asia (except India), with a value of 0.36 years, and the largest overestimation in Turkey/Arabia, with a value of 0.66 years. In boys, the smallest average overestimation was found in India (0.45 years), but Asia (except India), Africa - both with 0.46 years and Oceania - with 0.47 years were close, while the largest overestimation was found in Turkey/Arabia, with an average value of 0.63 years. The Demirjian method overestimated the age by about half a year for both sexes. Even if there are some geographical/ethnic differences, they are rather small, making the method useful irrespective of the ethnic profile of the subjects.

Age estimation in the living: A scoping review of population data for skeletal and dental methods

Age estimation of living individuals has become a crucial part of the forensic practice, especially due to the global increase in cross-border migration. The low rate of birth registration in many countries, hence of identification documents of migrants, especially in Africa and Asia, highlights the importance of reliable methods for age estimation of living individuals. Despite the fact that a number of skeletal and dental methods for age estimation have been developed, their main limitation is that they are based on specific reference samples and there is still no consensus among researchers on whether these methods can be applied to all populations. Though this issue remains still unsolved, population information at a glance could be useful for forensic practitioners dealing with such issues. This study aims at presenting a scoping review and mapping of the current situation concerning population data for skeletal (hand-wrist and clavicle) and dental methods (teeth eruption and third molar formation) for age estimation in the living. Two hundred studies on the rate of skeletal maturation and four hundred thirty-nine on the rate of dental maturation were found, covering the period from 1952 and 2020 for a total of ninety-eight countries. For most of the western and central African countries there are currently no data on the rate of skeletal and dental maturation. The same applies to the countries of the Middle East, as well as the eastern European countries, especially as regard the skeletal development.

Dental age estimation in Somali children and sub-adults combining permanent teeth and third molar development

Estimation of an individual’s age has important applications in forensics. In young individuals, it often relies on separate evaluations of permanent teeth (PT) and third molars (TM) development. Here, we analysed the age prediction performance of combined information from PT and TM in an unusual sample of healthy Somalis, born and living in Finland. PT development was staged according to Demirjian et al. (Hum Biol, 1973) and TM development according to Köhler et al. (Ann Anat, 1994), using panoramic radiographs from 803 subjects (397 males, 406 females) aged 3–23 years. A sex-specific Bayesian age-estimation model for the multivariate distribution of the stages conditional on age was fitted on PT, TM and PT and TM combined. The age-estimation performances were validated and quantified. The approach combining PT and TM only overestimated age with an ME of − 0.031 years in males and − 0.011 years in females, indicating the best age prediction performance.