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Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases
Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The use of retinal images, such as fundus photographs, is a promising approach for the development of AI-based diagnostic platforms. Retinal pathologies usually occur in a broad spectrum of eye diseases, includi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8142986/ https://www.ncbi.nlm.nih.gov/pubmed/33918998 http://dx.doi.org/10.3390/jpm11050321 |
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author | Kim, Kyoung Min Heo, Tae-Young Kim, Aesul Kim, Joohee Han, Kyu Jin Yun, Jaesuk Min, Jung Kee |
author_facet | Kim, Kyoung Min Heo, Tae-Young Kim, Aesul Kim, Joohee Han, Kyu Jin Yun, Jaesuk Min, Jung Kee |
author_sort | Kim, Kyoung Min |
collection | PubMed |
description | Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The use of retinal images, such as fundus photographs, is a promising approach for the development of AI-based diagnostic platforms. Retinal pathologies usually occur in a broad spectrum of eye diseases, including neovascular or dry age-related macular degeneration, epiretinal membrane, rhegmatogenous retinal detachment, retinitis pigmentosa, macular hole, retinal vein occlusions, and diabetic retinopathy. Here, we report a fundus image-based AI model for differential diagnosis of retinal diseases. We classified retinal images with three convolutional neural network models: ResNet50, VGG19, and Inception v3. Furthermore, the performance of several dense (fully connected) layers was compared. The prediction accuracy for diagnosis of nine classes of eight retinal diseases and normal control was 87.42% in the ResNet50 model, which added a dense layer with 128 nodes. Furthermore, our AI tool augments ophthalmologist’s performance in the diagnosis of retinal disease. These results suggested that the fundus image-based AI tool is applicable for the medical diagnosis process of retinal diseases. |
format | Online Article Text |
id | pubmed-8142986 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81429862021-05-25 Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases Kim, Kyoung Min Heo, Tae-Young Kim, Aesul Kim, Joohee Han, Kyu Jin Yun, Jaesuk Min, Jung Kee J Pers Med Article Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The use of retinal images, such as fundus photographs, is a promising approach for the development of AI-based diagnostic platforms. Retinal pathologies usually occur in a broad spectrum of eye diseases, including neovascular or dry age-related macular degeneration, epiretinal membrane, rhegmatogenous retinal detachment, retinitis pigmentosa, macular hole, retinal vein occlusions, and diabetic retinopathy. Here, we report a fundus image-based AI model for differential diagnosis of retinal diseases. We classified retinal images with three convolutional neural network models: ResNet50, VGG19, and Inception v3. Furthermore, the performance of several dense (fully connected) layers was compared. The prediction accuracy for diagnosis of nine classes of eight retinal diseases and normal control was 87.42% in the ResNet50 model, which added a dense layer with 128 nodes. Furthermore, our AI tool augments ophthalmologist’s performance in the diagnosis of retinal disease. These results suggested that the fundus image-based AI tool is applicable for the medical diagnosis process of retinal diseases. MDPI 2021-04-21 /pmc/articles/PMC8142986/ /pubmed/33918998 http://dx.doi.org/10.3390/jpm11050321 Text en © 2021 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 Kim, Kyoung Min Heo, Tae-Young Kim, Aesul Kim, Joohee Han, Kyu Jin Yun, Jaesuk Min, Jung Kee Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases |
title | Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases |
title_full | Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases |
title_fullStr | Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases |
title_full_unstemmed | Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases |
title_short | Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases |
title_sort | development of a fundus image-based deep learning diagnostic tool for various retinal diseases |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8142986/ https://www.ncbi.nlm.nih.gov/pubmed/33918998 http://dx.doi.org/10.3390/jpm11050321 |
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