A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos

Abstract Routine dental visit is the most common approach to detect the gingivitis. However, such diagnosis can sometimes be unavailable due to the limited medical resources in certain areas and costly for low-income populations. This study proposes to screen the existence of gingivitis and its irri...

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Autores principales:	Wen Li, Yuan Liang, Xuan Zhang, Chao Liu, Lei He, Leiying Miao, Weibin Sun
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bce3d4bc42a24994b1cf0e1973c1aabb
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spelling	oai:doaj.org-article:bce3d4bc42a24994b1cf0e1973c1aabb2021-12-02T18:51:52ZA deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos10.1038/s41598-021-96091-32045-2322https://doaj.org/article/bce3d4bc42a24994b1cf0e1973c1aabb2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96091-3https://doaj.org/toc/2045-2322Abstract Routine dental visit is the most common approach to detect the gingivitis. However, such diagnosis can sometimes be unavailable due to the limited medical resources in certain areas and costly for low-income populations. This study proposes to screen the existence of gingivitis and its irritants, i.e., dental calculus and soft deposits, from oral photos with a novel Multi-Task Learning convolutional neural network (CNN) model. The study can be meaningful for promoting the public dental health, since it sheds light on a cost-effective and ubiquitous solution for the early detection of dental issues. With 625 patients included in this study, the classification Area Under the Curve (AUC) for detecting gingivitis, dental calculus and soft deposits were 87.11%, 80.11%, and 78.57%, respectively; Meanwhile, according to our experiments, the model can also localize the three types of findings on oral photos with moderate accuracy, which enables the model to explain the screen results. By comparing to general-purpose CNNs, we showed our model significantly outperformed on both classification and localization tasks, which indicates the effectiveness of Multi-Task Learning on dental disease detection. In all, the study shows the potential of deep learning for enabling the screening of dental diseases among large populations.Wen LiYuan LiangXuan ZhangChao LiuLei HeLeiying MiaoWeibin SunNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wen Li Yuan Liang Xuan Zhang Chao Liu Lei He Leiying Miao Weibin Sun A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos
description	Abstract Routine dental visit is the most common approach to detect the gingivitis. However, such diagnosis can sometimes be unavailable due to the limited medical resources in certain areas and costly for low-income populations. This study proposes to screen the existence of gingivitis and its irritants, i.e., dental calculus and soft deposits, from oral photos with a novel Multi-Task Learning convolutional neural network (CNN) model. The study can be meaningful for promoting the public dental health, since it sheds light on a cost-effective and ubiquitous solution for the early detection of dental issues. With 625 patients included in this study, the classification Area Under the Curve (AUC) for detecting gingivitis, dental calculus and soft deposits were 87.11%, 80.11%, and 78.57%, respectively; Meanwhile, according to our experiments, the model can also localize the three types of findings on oral photos with moderate accuracy, which enables the model to explain the screen results. By comparing to general-purpose CNNs, we showed our model significantly outperformed on both classification and localization tasks, which indicates the effectiveness of Multi-Task Learning on dental disease detection. In all, the study shows the potential of deep learning for enabling the screening of dental diseases among large populations.
format	article
author	Wen Li Yuan Liang Xuan Zhang Chao Liu Lei He Leiying Miao Weibin Sun
author_facet	Wen Li Yuan Liang Xuan Zhang Chao Liu Lei He Leiying Miao Weibin Sun
author_sort	Wen Li
title	A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos
title_short	A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos
title_full	A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos
title_fullStr	A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos
title_full_unstemmed	A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos
title_sort	deep learning approach to automatic gingivitis screening based on classification and localization in rgb photos
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/bce3d4bc42a24994b1cf0e1973c1aabb
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A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos

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