Automated severity scoring of atopic dermatitis patients by a deep neural network

Abstract Scoring atopic dermatitis (AD) severity with the Eczema Area and Severity Index (EASI) in an objective and reproducible manner is challenging. Automated measurement of erythema, papulation, excoriation, and lichenification severity using images has not yet been investigated. Our aim was to...

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Autores principales:	Chul Hwan Bang, Jae Woong Yoon, Jae Yeon Ryu, Jae Heon Chun, Ju Hee Han, Young Bok Lee, Jun Young Lee, Young Min Park, Suk Jun Lee, Ji Hyun Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/eb62cc76b50b46fcba98a52394a5c359
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spelling	oai:doaj.org-article:eb62cc76b50b46fcba98a52394a5c3592021-12-02T16:31:11ZAutomated severity scoring of atopic dermatitis patients by a deep neural network10.1038/s41598-021-85489-82045-2322https://doaj.org/article/eb62cc76b50b46fcba98a52394a5c3592021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85489-8https://doaj.org/toc/2045-2322Abstract Scoring atopic dermatitis (AD) severity with the Eczema Area and Severity Index (EASI) in an objective and reproducible manner is challenging. Automated measurement of erythema, papulation, excoriation, and lichenification severity using images has not yet been investigated. Our aim was to determine whether convolutional neural networks (CNNs) could assess erythema, papulation, excoriation, and lichenification severity at a level of competence comparable to dermatologists. We created a standard dataset of 8,000 clinical images showing AD. Each component of the EASI was scored from 0 to 3 by three dermatologists. We trained four CNNs (ResNet V1, ResNet V2, GoogLeNet, and VGG-Net) with the image dataset and determined which CNN was the most suitable for erythema, papulation, excoriation, and lichenification scoring. The brightness of the images in each dataset was adjusted to − 80% to + 80% of the original brightness (i.e., 9 levels by 20%) to investigate if the CNNs accurately measured scores if image brightness levels were changed. Compared to the dermatologists’ scoring, accuracy rates of the CNNs were 99.17% for erythema, 93.17% for papulation, 96.00% for excoriation, and 97.17% for lichenification. CNNs trained with brightness-adjusted images achieved a high accuracy without the need to standardize camera settings. These results suggested that CNNs perform at level of competence comparable to dermatologists for scoring erythema, papulation, excoriation, and lichenification severity.Chul Hwan BangJae Woong YoonJae Yeon RyuJae Heon ChunJu Hee HanYoung Bok LeeJun Young LeeYoung Min ParkSuk Jun LeeJi Hyun LeeNature 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 Chul Hwan Bang Jae Woong Yoon Jae Yeon Ryu Jae Heon Chun Ju Hee Han Young Bok Lee Jun Young Lee Young Min Park Suk Jun Lee Ji Hyun Lee Automated severity scoring of atopic dermatitis patients by a deep neural network
description	Abstract Scoring atopic dermatitis (AD) severity with the Eczema Area and Severity Index (EASI) in an objective and reproducible manner is challenging. Automated measurement of erythema, papulation, excoriation, and lichenification severity using images has not yet been investigated. Our aim was to determine whether convolutional neural networks (CNNs) could assess erythema, papulation, excoriation, and lichenification severity at a level of competence comparable to dermatologists. We created a standard dataset of 8,000 clinical images showing AD. Each component of the EASI was scored from 0 to 3 by three dermatologists. We trained four CNNs (ResNet V1, ResNet V2, GoogLeNet, and VGG-Net) with the image dataset and determined which CNN was the most suitable for erythema, papulation, excoriation, and lichenification scoring. The brightness of the images in each dataset was adjusted to − 80% to + 80% of the original brightness (i.e., 9 levels by 20%) to investigate if the CNNs accurately measured scores if image brightness levels were changed. Compared to the dermatologists’ scoring, accuracy rates of the CNNs were 99.17% for erythema, 93.17% for papulation, 96.00% for excoriation, and 97.17% for lichenification. CNNs trained with brightness-adjusted images achieved a high accuracy without the need to standardize camera settings. These results suggested that CNNs perform at level of competence comparable to dermatologists for scoring erythema, papulation, excoriation, and lichenification severity.
format	article
author	Chul Hwan Bang Jae Woong Yoon Jae Yeon Ryu Jae Heon Chun Ju Hee Han Young Bok Lee Jun Young Lee Young Min Park Suk Jun Lee Ji Hyun Lee
author_facet	Chul Hwan Bang Jae Woong Yoon Jae Yeon Ryu Jae Heon Chun Ju Hee Han Young Bok Lee Jun Young Lee Young Min Park Suk Jun Lee Ji Hyun Lee
author_sort	Chul Hwan Bang
title	Automated severity scoring of atopic dermatitis patients by a deep neural network
title_short	Automated severity scoring of atopic dermatitis patients by a deep neural network
title_full	Automated severity scoring of atopic dermatitis patients by a deep neural network
title_fullStr	Automated severity scoring of atopic dermatitis patients by a deep neural network
title_full_unstemmed	Automated severity scoring of atopic dermatitis patients by a deep neural network
title_sort	automated severity scoring of atopic dermatitis patients by a deep neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/eb62cc76b50b46fcba98a52394a5c359
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Automated severity scoring of atopic dermatitis patients by a deep neural network

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