Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images

Abstract Visually impaired and blind people due to diabetic retinopathy were 2.6 million in 2015 and estimated to be 3.2 million in 2020 globally. Though the incidence of diabetic retinopathy is expected to decrease for high-income countries, detection and treatment of it in the early stages are cru...

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Autores principales:	Kangrok Oh, Hae Min Kang, Dawoon Leem, Hyungyu Lee, Kyoung Yul Seo, Sangchul Yoon
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7dd1c9fa24774e6883b19d0d865f6e8a
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spelling	oai:doaj.org-article:7dd1c9fa24774e6883b19d0d865f6e8a2021-12-02T13:56:47ZEarly detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images10.1038/s41598-021-81539-32045-2322https://doaj.org/article/7dd1c9fa24774e6883b19d0d865f6e8a2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81539-3https://doaj.org/toc/2045-2322Abstract Visually impaired and blind people due to diabetic retinopathy were 2.6 million in 2015 and estimated to be 3.2 million in 2020 globally. Though the incidence of diabetic retinopathy is expected to decrease for high-income countries, detection and treatment of it in the early stages are crucial for low-income and middle-income countries. Due to the recent advancement of deep learning technologies, researchers showed that automated screening and grading of diabetic retinopathy are efficient in saving time and workforce. However, most automatic systems utilize conventional fundus photography, despite ultra-wide-field fundus photography provides up to 82% of the retinal surface. In this study, we present a diabetic retinopathy detection system based on ultra-wide-field fundus photography and deep learning. In experiments, we show that the use of early treatment diabetic retinopathy study 7-standard field image extracted from ultra-wide-field fundus photography outperforms that of the optic disc and macula centered image in a statistical sense.Kangrok OhHae Min KangDawoon LeemHyungyu LeeKyoung Yul SeoSangchul YoonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kangrok Oh Hae Min Kang Dawoon Leem Hyungyu Lee Kyoung Yul Seo Sangchul Yoon Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images
description	Abstract Visually impaired and blind people due to diabetic retinopathy were 2.6 million in 2015 and estimated to be 3.2 million in 2020 globally. Though the incidence of diabetic retinopathy is expected to decrease for high-income countries, detection and treatment of it in the early stages are crucial for low-income and middle-income countries. Due to the recent advancement of deep learning technologies, researchers showed that automated screening and grading of diabetic retinopathy are efficient in saving time and workforce. However, most automatic systems utilize conventional fundus photography, despite ultra-wide-field fundus photography provides up to 82% of the retinal surface. In this study, we present a diabetic retinopathy detection system based on ultra-wide-field fundus photography and deep learning. In experiments, we show that the use of early treatment diabetic retinopathy study 7-standard field image extracted from ultra-wide-field fundus photography outperforms that of the optic disc and macula centered image in a statistical sense.
format	article
author	Kangrok Oh Hae Min Kang Dawoon Leem Hyungyu Lee Kyoung Yul Seo Sangchul Yoon
author_facet	Kangrok Oh Hae Min Kang Dawoon Leem Hyungyu Lee Kyoung Yul Seo Sangchul Yoon
author_sort	Kangrok Oh
title	Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images
title_short	Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images
title_full	Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images
title_fullStr	Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images
title_full_unstemmed	Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images
title_sort	early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7dd1c9fa24774e6883b19d0d865f6e8a
work_keys_str_mv	AT kangrokoh earlydetectionofdiabeticretinopathybasedondeeplearningandultrawidefieldfundusimages AT haeminkang earlydetectionofdiabeticretinopathybasedondeeplearningandultrawidefieldfundusimages AT dawoonleem earlydetectionofdiabeticretinopathybasedondeeplearningandultrawidefieldfundusimages AT hyungyulee earlydetectionofdiabeticretinopathybasedondeeplearningandultrawidefieldfundusimages AT kyoungyulseo earlydetectionofdiabeticretinopathybasedondeeplearningandultrawidefieldfundusimages AT sangchulyoon earlydetectionofdiabeticretinopathybasedondeeplearningandultrawidefieldfundusimages
_version_	1718392388028727296

Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images

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