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...
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| Veröffentlicht in: | Journal of personalized medicine 2021-04, Vol.11 (5), p.321 |
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| container_title | Journal of personalized medicine |
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| creator | Kim, Kyoung Min Heo, Tae-Young Kim, Aesul Kim, Joohee Han, Kyu Jin Yun, Jaesuk Min, Jung Kee |
| 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. |
| doi_str_mv | 10.3390/jpm11050321 |
| format | Article |
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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.</description><identifier>ISSN: 2075-4426</identifier><identifier>EISSN: 2075-4426</identifier><identifier>DOI: 10.3390/jpm11050321</identifier><identifier>PMID: 33918998</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Age ; Artificial intelligence ; Deep learning ; Diabetes ; Diabetes mellitus ; Diabetic retinopathy ; Differential diagnosis ; Disease ; Eye diseases ; Hemorrhage ; Macular degeneration ; Medical imaging ; Medical records ; Neural networks ; Photography ; Precision medicine ; Retina ; Retinitis ; Retinitis pigmentosa ; Retinopathy</subject><ispartof>Journal of personalized medicine, 2021-04, Vol.11 (5), p.321</ispartof><rights>2021 by the authors. 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/). 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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.</description><subject>Age</subject><subject>Artificial intelligence</subject><subject>Deep learning</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetic retinopathy</subject><subject>Differential diagnosis</subject><subject>Disease</subject><subject>Eye diseases</subject><subject>Hemorrhage</subject><subject>Macular degeneration</subject><subject>Medical imaging</subject><subject>Medical records</subject><subject>Neural networks</subject><subject>Photography</subject><subject>Precision medicine</subject><subject>Retina</subject><subject>Retinitis</subject><subject>Retinitis pigmentosa</subject><subject>Retinopathy</subject><issn>2075-4426</issn><issn>2075-4426</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpdkUtLxDAUhYMoKurKvQTcCFLNo80kG0FnfMGAIOrSkLY3Y4a2qUkr-O-N-GD0bnIh3zncw0Fon5ITzhU5XfYtpaQgnNE1tM3IpMjynIn1lX0L7cW4JGlkwZggm2grSalUSm6j5xm8QeP7FroBe4sNvhq7eoz4tjULyC5MhBrPAHo8BxM61y3wzJlF5-PgKvzgfYOtD_jJBOeT6h4G15kmMRGSNO6iDWuaCHvf7w56vLp8mN5k87vr2-n5PKtyooaMS2srLhjApKaFkpSa2sqyVBY4KW0pq7yszSQvGSst5JJwkEqQQhBRqMpwvoPOvnz7sWyhrlKaYBrdB9ea8K69cfrvT-de9MK_aUlzpqRIBkffBsG_jhAH3bpYQdOYDlIwzQpG5KQgSiX08B-69GNIqT8pzlIgIWSijr-oKvgYA9jfYyjRn9XpleoSfbB6_y_7UxT_AKsBlKw</recordid><startdate>20210421</startdate><enddate>20210421</enddate><creator>Kim, Kyoung Min</creator><creator>Heo, Tae-Young</creator><creator>Kim, Aesul</creator><creator>Kim, Joohee</creator><creator>Han, Kyu Jin</creator><creator>Yun, Jaesuk</creator><creator>Min, Jung Kee</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FH</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-8006-8560</orcidid><orcidid>https://orcid.org/0000-0002-0505-5559</orcidid></search><sort><creationdate>20210421</creationdate><title>Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases</title><author>Kim, Kyoung Min ; Heo, Tae-Young ; Kim, Aesul ; Kim, Joohee ; Han, Kyu Jin ; Yun, Jaesuk ; Min, Jung Kee</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c409t-38ffc362ee7d159811adf8bb9fe30bfb8c4bda74b22bfe4803e8960560659ca33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Age</topic><topic>Artificial intelligence</topic><topic>Deep learning</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetic retinopathy</topic><topic>Differential diagnosis</topic><topic>Disease</topic><topic>Eye diseases</topic><topic>Hemorrhage</topic><topic>Macular degeneration</topic><topic>Medical imaging</topic><topic>Medical records</topic><topic>Neural networks</topic><topic>Photography</topic><topic>Precision medicine</topic><topic>Retina</topic><topic>Retinitis</topic><topic>Retinitis pigmentosa</topic><topic>Retinopathy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Kyoung Min</creatorcontrib><creatorcontrib>Heo, Tae-Young</creatorcontrib><creatorcontrib>Kim, Aesul</creatorcontrib><creatorcontrib>Kim, Joohee</creatorcontrib><creatorcontrib>Han, Kyu Jin</creatorcontrib><creatorcontrib>Yun, Jaesuk</creatorcontrib><creatorcontrib>Min, Jung Kee</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of personalized medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Kyoung Min</au><au>Heo, Tae-Young</au><au>Kim, Aesul</au><au>Kim, Joohee</au><au>Han, Kyu Jin</au><au>Yun, Jaesuk</au><au>Min, Jung Kee</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases</atitle><jtitle>Journal of personalized medicine</jtitle><addtitle>J Pers Med</addtitle><date>2021-04-21</date><risdate>2021</risdate><volume>11</volume><issue>5</issue><spage>321</spage><pages>321-</pages><issn>2075-4426</issn><eissn>2075-4426</eissn><abstract>Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. 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| subjects | Age Artificial intelligence Deep learning Diabetes Diabetes mellitus Diabetic retinopathy Differential diagnosis Disease Eye diseases Hemorrhage Macular degeneration Medical imaging Medical records Neural networks Photography Precision medicine Retina Retinitis Retinitis pigmentosa Retinopathy |
| title | Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases |
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