Reflectance and color prediction of dental material monolithic samples with varying thickness-Reference-Cited by-同舟云学术

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Reflectance and color prediction of dental material monolithic samples with varying thickness

Published:2022-04 Issue:4 Volume:38 Page:622-631
ISSN:0109-5641
Container-title:Dental Materials
language:en
Short-container-title:Dental Materials

Author:

Tejada-Casado María,Ghinea Razvan,Perez María M.,Lübbe Henning,Pop-Ciutrila Ioana S.,Ruiz-López Javier,Herrera Luis Javier

Funder

Spain Ministry of Science and Innovation

Ministerio de Ciencia e Innovación

Universidad de Granada

European Regional Development Fund

Junta de Andalucía

Publisher

Elsevier BV

Subject

Mechanics of Materials,General Dentistry,General Materials Science

Reference45 articles.

1. Review of translucency determinations and applications to dent mater;WM;J Esthet Restor Dent,2014

2. In vivo study for tooth colour determination–visual versus digital;J;Clin Oral Invest,2017

3. Dental color matching instruments and systems. Review of clinical and research aspects;SJ;J Dent,2010

4. Visual and instrumental shade matching using cielab and ciede2000 color difference formulas;OE;Dent Mater,2016

5. Dental shade guide variability for hues b, c, and d using cross-polarized photography;CS;Int J Periodontics Restor Dent,2018

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1. Comparative analysis of optical and numerical models for reflectance and color prediction of monolithic dental resin composites with varying thicknesses;Dental Materials;2024-10

2. Effect of immune-boosting beverage, energy beverage, hydrogen peroxide superior, polishing methods and fine-grained dental prophylaxis paste on color of CAD-CAM restorative materials;2024-07-29

3. Fusion machine learning model predicts CAD-CAM ceramic colors and the corresponding minimal thicknesses over various clinical backgrounds;Dental Materials;2023-11

4. Performance of two-flux and four-flux models for predicting the spectral reflectance and transmittance factors of flowable dental resin composites;Dental Materials;2023-08

5. Dataset artificial augmentation with a small number of training samples for reflectance estimation;Optics Express;2023-02-17

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