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

Abstract

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.

Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years

“…To the present, the most recent study on the interface between artificial intelligence and dental age estimation was a systematic review 42 . The study has revealed that methods performed “manually” (operator only) tend to generate more marked overestimations (e.g.…”

Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years

“…To the present, the most recent study on the interface between arti cial intelligence and dental age estimation was a systematic review 42 . The study has revealed that methods performed "manually" (operator only) tend to generate more marked overestimations (e.g.…”

Accuracy of Age Estimation Using Three Dental Age Estimation Methods in a Young, Large, and Multiethnic Patient Sample

“…Modern dental age estimation may achieve better accuracy using statistical tools such as regression and Bayesian calibration or even using artificial-intelligence-based approaches [31]. These methods require strict evaluations of their accuracy, especially for children and young adults aged around 18 years old, if their goal is to help evaluate an individual's minority according to the United Nations Convention on the Rights of the Child.…”