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Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases

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Abstract
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.
Author(s)
김경민허태영김애슬김주희한규진윤재석민정기
Issued Date
2021
Type
Article
Keyword
artificial intelligenceclass activation mapconvolutional neural networkfundus photographretinal diseases
DOI
10.3390/jpm11050321
URI
https://oak.ulsan.ac.kr/handle/2021.oak/7652
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_92a804e4c87d4b6e890c147c11d5c5fc&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Development%20of%20a%20Fundus%20Image-Based%20Deep%20Learning%20Diagnostic%20Tool%20for%20Various%20Retinal%20Diseases&offset=0&pcAvailability=true
Publisher
JOURNAL OF PERSONALIZED MEDICINE
Location
스위스
Language
영어
ISSN
2075-4426
Citation Volume
11
Citation Number
5
Citation Start Page
0
Citation End Page
0
Appears in Collections:
Medicine > Medicine
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