A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography

Abstract As the prevalence of diabetes increases, millions of people need to be screened for diabetic retinopathy (DR). Remarkable advances in technology have made it possible to use artificial intelligence to screen DR from retinal images with high accuracy and reliability, resulting in reducing hu...

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Autores principales:	Gahyung Ryu, Kyungmin Lee, Donggeun Park, Sang Hyun Park, Min Sagong
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ff73611e942b4f6ba17d9ea59c8045d2
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spelling	oai:doaj.org-article:ff73611e942b4f6ba17d9ea59c8045d22021-11-28T12:17:14ZA deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography10.1038/s41598-021-02479-62045-2322https://doaj.org/article/ff73611e942b4f6ba17d9ea59c8045d22021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02479-6https://doaj.org/toc/2045-2322Abstract As the prevalence of diabetes increases, millions of people need to be screened for diabetic retinopathy (DR). Remarkable advances in technology have made it possible to use artificial intelligence to screen DR from retinal images with high accuracy and reliability, resulting in reducing human labor by processing large amounts of data in a shorter time. We developed a fully automated classification algorithm to diagnose DR and identify referable status using optical coherence tomography angiography (OCTA) images with convolutional neural network (CNN) model and verified its feasibility by comparing its performance with that of conventional machine learning model. Ground truths for classifications were made based on ultra-widefield fluorescein angiography to increase the accuracy of data annotation. The proposed CNN classifier achieved an accuracy of 91–98%, a sensitivity of 86–97%, a specificity of 94–99%, and an area under the curve of 0.919–0.976. In the external validation, overall similar performances were also achieved. The results were similar regardless of the size and depth of the OCTA images, indicating that DR could be satisfactorily classified even with images comprising narrow area of the macular region and a single image slab of retina. The CNN-based classification using OCTA is expected to create a novel diagnostic workflow for DR detection and referral.Gahyung RyuKyungmin LeeDonggeun ParkSang Hyun ParkMin SagongNature 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 Gahyung Ryu Kyungmin Lee Donggeun Park Sang Hyun Park Min Sagong A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography
description	Abstract As the prevalence of diabetes increases, millions of people need to be screened for diabetic retinopathy (DR). Remarkable advances in technology have made it possible to use artificial intelligence to screen DR from retinal images with high accuracy and reliability, resulting in reducing human labor by processing large amounts of data in a shorter time. We developed a fully automated classification algorithm to diagnose DR and identify referable status using optical coherence tomography angiography (OCTA) images with convolutional neural network (CNN) model and verified its feasibility by comparing its performance with that of conventional machine learning model. Ground truths for classifications were made based on ultra-widefield fluorescein angiography to increase the accuracy of data annotation. The proposed CNN classifier achieved an accuracy of 91–98%, a sensitivity of 86–97%, a specificity of 94–99%, and an area under the curve of 0.919–0.976. In the external validation, overall similar performances were also achieved. The results were similar regardless of the size and depth of the OCTA images, indicating that DR could be satisfactorily classified even with images comprising narrow area of the macular region and a single image slab of retina. The CNN-based classification using OCTA is expected to create a novel diagnostic workflow for DR detection and referral.
format	article
author	Gahyung Ryu Kyungmin Lee Donggeun Park Sang Hyun Park Min Sagong
author_facet	Gahyung Ryu Kyungmin Lee Donggeun Park Sang Hyun Park Min Sagong
author_sort	Gahyung Ryu
title	A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography
title_short	A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography
title_full	A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography
title_fullStr	A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography
title_full_unstemmed	A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography
title_sort	deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ff73611e942b4f6ba17d9ea59c8045d2
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A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography

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