This study focuses on the evaluation of factors influencing the quality (accuracy and reliability) of non-adult dental age assessment from radiographic stages of permanent teeth (excluding the third molar). We used four distinct cross-sectional samples of 1,528 healthy children: 3 of known geographic origin (Ivory Coast, Iran and France) and 1 additional sample of children whose grandparents originated from a different continent. Two different methods of calculations are compared: the correspondence analysis combined with linear regression (CAR) and Bayesian predictions (with no independence assumption). Our results indicate that the quality of age assessment does not seem to depend predominantly on the use of geographic-specific standards. In the case of Bayesian predictions, we observed a clear trend in favour of significantly higher accuracy and reliability levels when using non-geographic-specific standards. One of the main advantage of Bayesian predictions over maximum likelihood methods of estimation is an overall increase in accuracy with high levels of reliability on a fraction of the test sample and, importantly, across all age categories (contrary to methods based on regression analysis). Importantly, in the case of Bayesian non-adult predictions, and contrary to age estimation techniques based on regression, a better quality does not depend on age.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.