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A Smartphone Application for Personalized Tooth Shade Determination
Tooth shade determination methods for evaluating the effectiveness of whitening products at home are limited. In this study, an iPhone app for personalized tooth shade determination was developed. While capturing dental photographs in selfie mode before and after whitening, the app can maintain cons...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10252511/ https://www.ncbi.nlm.nih.gov/pubmed/37296821 http://dx.doi.org/10.3390/diagnostics13111969 |
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author | Kusayanagi, Tomoya Maegawa, Sota Terauchi, Shuya Hashimoto, Wataru Kaneda, Shohei |
author_facet | Kusayanagi, Tomoya Maegawa, Sota Terauchi, Shuya Hashimoto, Wataru Kaneda, Shohei |
author_sort | Kusayanagi, Tomoya |
collection | PubMed |
description | Tooth shade determination methods for evaluating the effectiveness of whitening products at home are limited. In this study, an iPhone app for personalized tooth shade determination was developed. While capturing dental photographs in selfie mode before and after whitening, the app can maintain consistent illumination and tooth appearance conditions that affect tooth color measurement. An ambient light sensor was used to standardize the illumination conditions. To maintain consistent tooth appearance conditions determined by appropriately opening the mouth, facial landmark detection, an artificial intelligence technique that estimates key face parts and outlines, was used. The effectiveness of the app in ensuring uniform tooth appearance was investigated through color measurements of the upper incisors of seven participants via photographs captured in succession. The coefficients of variation for incisors L*, a*, and b* were less than 0.0256 (95% CI, 0.0173–0.0338), 0.2748 (0.1596–0.3899), and 0.1053 (0.0078–0.2028), respectively. To examine the feasibility of the app for tooth shade determination, gel whitening after pseudo-staining by coffee and grape juice was performed. Consequently, whitening results were evaluated by monitoring the ∆E(ab) color difference values (1.3 unit minimum). Although tooth shade determination remains a relative quantification method, the proposed method can support evidence-based selection of whitening products. |
format | Online Article Text |
id | pubmed-10252511 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102525112023-06-10 A Smartphone Application for Personalized Tooth Shade Determination Kusayanagi, Tomoya Maegawa, Sota Terauchi, Shuya Hashimoto, Wataru Kaneda, Shohei Diagnostics (Basel) Article Tooth shade determination methods for evaluating the effectiveness of whitening products at home are limited. In this study, an iPhone app for personalized tooth shade determination was developed. While capturing dental photographs in selfie mode before and after whitening, the app can maintain consistent illumination and tooth appearance conditions that affect tooth color measurement. An ambient light sensor was used to standardize the illumination conditions. To maintain consistent tooth appearance conditions determined by appropriately opening the mouth, facial landmark detection, an artificial intelligence technique that estimates key face parts and outlines, was used. The effectiveness of the app in ensuring uniform tooth appearance was investigated through color measurements of the upper incisors of seven participants via photographs captured in succession. The coefficients of variation for incisors L*, a*, and b* were less than 0.0256 (95% CI, 0.0173–0.0338), 0.2748 (0.1596–0.3899), and 0.1053 (0.0078–0.2028), respectively. To examine the feasibility of the app for tooth shade determination, gel whitening after pseudo-staining by coffee and grape juice was performed. Consequently, whitening results were evaluated by monitoring the ∆E(ab) color difference values (1.3 unit minimum). Although tooth shade determination remains a relative quantification method, the proposed method can support evidence-based selection of whitening products. MDPI 2023-06-05 /pmc/articles/PMC10252511/ /pubmed/37296821 http://dx.doi.org/10.3390/diagnostics13111969 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kusayanagi, Tomoya Maegawa, Sota Terauchi, Shuya Hashimoto, Wataru Kaneda, Shohei A Smartphone Application for Personalized Tooth Shade Determination |
title | A Smartphone Application for Personalized Tooth Shade Determination |
title_full | A Smartphone Application for Personalized Tooth Shade Determination |
title_fullStr | A Smartphone Application for Personalized Tooth Shade Determination |
title_full_unstemmed | A Smartphone Application for Personalized Tooth Shade Determination |
title_short | A Smartphone Application for Personalized Tooth Shade Determination |
title_sort | smartphone application for personalized tooth shade determination |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10252511/ https://www.ncbi.nlm.nih.gov/pubmed/37296821 http://dx.doi.org/10.3390/diagnostics13111969 |
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