A machine learning approach to investigate the materials science of enamel aging-Reference-Cited by-同舟云学术

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A machine learning approach to investigate the materials science of enamel aging

Published:2021-12 Issue:12 Volume:37 Page:1761-1771
ISSN:0109-5641
Container-title:Dental Materials
language:en
Short-container-title:Dental Materials

Author:

Yan W.,Renteria C.^ORCID,Huang Y.,Arola Dwayne D.^ORCID

Funder

University of Washington

National Science Foundation

Clean Energy Institute

National Institutes of Health

Molecular Engineering and Sciences Institute, University of Washington

Colgate-Palmolive Company

Publisher

Elsevier BV

Subject

Mechanics of Materials,General Dentistry,General Materials Science

Reference51 articles.

1. Projections of U.S. Edentulism prevalence following 5 decades of decline;Slade;J Dent Res,2014

2. Geriatric oral health issues in the United States;Berkey;Int Dent J,2001

3. Prevalence of defective dental restorations;Moore;J Prosthet Dent,1967

4. Anterior composite restorations: a systematic review on long-term survival and reasons for failure;Demarco;Dent Mater,2015

5. Influence of patient factors on age of restorations at failure and reasons for their placement and replacement;Burke;J Dent,2001

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3. Machine Learning Analysis of Microtensile Bond Strength of Dental Adhesives;Journal of Dental Research;2023-07-18

4. Surface and Structural Studies of Age-Related Changes in Dental Enamel: An Animal Model;Materials;2022-06-03

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