Dual-input convolutional neural network for glaucoma diagnosis using spectral-domain optical coherence tomography
Background/AimsTo evaluate, with spectral-domain optical coherence tomography (SD-OCT), the glaucoma-diagnostic ability of a deep-learning classifier.MethodsA total of 777 Cirrus high-definition SD-OCT image sets of the retinal nerve fibre layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL)...
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description | Background/AimsTo evaluate, with spectral-domain optical coherence tomography (SD-OCT), the glaucoma-diagnostic ability of a deep-learning classifier.MethodsA total of 777 Cirrus high-definition SD-OCT image sets of the retinal nerve fibre layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL) of 315 normal subjects, 219 patients with early-stage primary open-angle glaucoma (POAG) and 243 patients with moderate-to-severe-stage POAG were aggregated. The image sets were divided into a training data set (252 normal, 174 early POAG and 195 moderate-to-severe POAG) and a test data set (63 normal, 45 early POAG and 48 moderate-to-severe POAG). The visual geometry group (VGG16)-based dual-input convolutional neural network (DICNN) was adopted for the glaucoma diagnoses. Unlike other networks, the DICNN structure takes two images (both RNFL and GCIPL) as inputs. The glaucoma-diagnostic ability was computed according to both accuracy and area under the receiver operating characteristic curve (AUC).ResultsFor the test data set, DICNN could distinguish between patients with glaucoma and normal subjects accurately (accuracy=92.793%, AUC=0.957 (95% CI 0.943 to 0.966), sensitivity=0.896 (95% CI 0.896 to 0.917), specificity=0.952 (95% CI 0.921 to 0.952)). For distinguishing between patients with early-stage glaucoma and normal subjects, DICNN’s diagnostic ability (accuracy=85.185%, AUC=0.869 (95% CI 0.825 to 0.879), sensitivity=0.921 (95% CI 0.813 to 0.905), specificity=0.756 (95% CI 0.610 to 0.790)]) was higher than convolutional neural network algorithms that trained with RNFL or GCIPL separately.ConclusionThe deep-learning algorithm using SD-OCT can distinguish normal subjects not only from established patients with glaucoma but also from patients with early-stage glaucoma. The deep-learning model with DICNN, as trained by both RNFL and GCIPL thickness map data, showed a high diagnostic ability for discriminatingpatients with early-stage glaucoma from normal subjects. |
doi_str_mv | 10.1136/bjophthalmol-2020-316274 |
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The image sets were divided into a training data set (252 normal, 174 early POAG and 195 moderate-to-severe POAG) and a test data set (63 normal, 45 early POAG and 48 moderate-to-severe POAG). The visual geometry group (VGG16)-based dual-input convolutional neural network (DICNN) was adopted for the glaucoma diagnoses. Unlike other networks, the DICNN structure takes two images (both RNFL and GCIPL) as inputs. The glaucoma-diagnostic ability was computed according to both accuracy and area under the receiver operating characteristic curve (AUC).ResultsFor the test data set, DICNN could distinguish between patients with glaucoma and normal subjects accurately (accuracy=92.793%, AUC=0.957 (95% CI 0.943 to 0.966), sensitivity=0.896 (95% CI 0.896 to 0.917), specificity=0.952 (95% CI 0.921 to 0.952)). For distinguishing between patients with early-stage glaucoma and normal subjects, DICNN’s diagnostic ability (accuracy=85.185%, AUC=0.869 (95% CI 0.825 to 0.879), sensitivity=0.921 (95% CI 0.813 to 0.905), specificity=0.756 (95% CI 0.610 to 0.790)]) was higher than convolutional neural network algorithms that trained with RNFL or GCIPL separately.ConclusionThe deep-learning algorithm using SD-OCT can distinguish normal subjects not only from established patients with glaucoma but also from patients with early-stage glaucoma. The deep-learning model with DICNN, as trained by both RNFL and GCIPL thickness map data, showed a high diagnostic ability for discriminatingpatients with early-stage glaucoma from normal subjects.</description><identifier>ISSN: 0007-1161</identifier><identifier>EISSN: 1468-2079</identifier><identifier>DOI: 10.1136/bjophthalmol-2020-316274</identifier><identifier>PMID: 32920530</identifier><language>eng</language><publisher>BMA House, Tavistock Square, London, WC1H 9JR: BMJ Publishing Group Ltd</publisher><subject>Algorithms ; Cardiovascular disease ; Classification ; Clinical science ; Deep learning ; Glaucoma ; Glaucoma - diagnostic imaging ; Glaucoma, Open-Angle - diagnostic imaging ; Humans ; Imaging ; Intraocular Pressure ; Macula ; Medical diagnosis ; Neural networks ; Neural Networks, Computer ; Optic Nerve ; Optics ; Retinal Ganglion Cells ; ROC Curve ; Tomography ; Tomography, Optical Coherence</subject><ispartof>British journal of ophthalmology, 2021-11, Vol.105 (11), p.1555-1560</ispartof><rights>Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.</rights><rights>Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.</rights><rights>2021 Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.</rights><rights>2020 Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b416t-26a38d4f2d1008e6fd0a4ce71b493c1608979da35ed81b5b6f7e02b81a0c734c3</citedby><cites>FETCH-LOGICAL-b416t-26a38d4f2d1008e6fd0a4ce71b493c1608979da35ed81b5b6f7e02b81a0c734c3</cites><orcidid>0000-0002-6037-8449 ; 0000-0002-8137-8843</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32920530$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sun, Sukkyu</creatorcontrib><creatorcontrib>Ha, Ahnul</creatorcontrib><creatorcontrib>Kim, Young Kook</creatorcontrib><creatorcontrib>Yoo, Byeong Wook</creatorcontrib><creatorcontrib>Kim, Hee Chan</creatorcontrib><creatorcontrib>Park, Ki Ho</creatorcontrib><title>Dual-input convolutional neural network for glaucoma diagnosis using spectral-domain optical coherence tomography</title><title>British journal of ophthalmology</title><addtitle>Br J Ophthalmol</addtitle><addtitle>Br J Ophthalmol</addtitle><description>Background/AimsTo evaluate, with spectral-domain optical coherence tomography (SD-OCT), the glaucoma-diagnostic ability of a deep-learning classifier.MethodsA total of 777 Cirrus high-definition SD-OCT image sets of the retinal nerve fibre layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL) of 315 normal subjects, 219 patients with early-stage primary open-angle glaucoma (POAG) and 243 patients with moderate-to-severe-stage POAG were aggregated. The image sets were divided into a training data set (252 normal, 174 early POAG and 195 moderate-to-severe POAG) and a test data set (63 normal, 45 early POAG and 48 moderate-to-severe POAG). The visual geometry group (VGG16)-based dual-input convolutional neural network (DICNN) was adopted for the glaucoma diagnoses. Unlike other networks, the DICNN structure takes two images (both RNFL and GCIPL) as inputs. The glaucoma-diagnostic ability was computed according to both accuracy and area under the receiver operating characteristic curve (AUC).ResultsFor the test data set, DICNN could distinguish between patients with glaucoma and normal subjects accurately (accuracy=92.793%, AUC=0.957 (95% CI 0.943 to 0.966), sensitivity=0.896 (95% CI 0.896 to 0.917), specificity=0.952 (95% CI 0.921 to 0.952)). For distinguishing between patients with early-stage glaucoma and normal subjects, DICNN’s diagnostic ability (accuracy=85.185%, AUC=0.869 (95% CI 0.825 to 0.879), sensitivity=0.921 (95% CI 0.813 to 0.905), specificity=0.756 (95% CI 0.610 to 0.790)]) was higher than convolutional neural network algorithms that trained with RNFL or GCIPL separately.ConclusionThe deep-learning algorithm using SD-OCT can distinguish normal subjects not only from established patients with glaucoma but also from patients with early-stage glaucoma. The deep-learning model with DICNN, as trained by both RNFL and GCIPL thickness map data, showed a high diagnostic ability for discriminatingpatients with early-stage glaucoma from normal subjects.</description><subject>Algorithms</subject><subject>Cardiovascular disease</subject><subject>Classification</subject><subject>Clinical science</subject><subject>Deep learning</subject><subject>Glaucoma</subject><subject>Glaucoma - diagnostic imaging</subject><subject>Glaucoma, Open-Angle - diagnostic imaging</subject><subject>Humans</subject><subject>Imaging</subject><subject>Intraocular Pressure</subject><subject>Macula</subject><subject>Medical diagnosis</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Optic Nerve</subject><subject>Optics</subject><subject>Retinal Ganglion Cells</subject><subject>ROC Curve</subject><subject>Tomography</subject><subject>Tomography, Optical Coherence</subject><issn>0007-1161</issn><issn>1468-2079</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqFkctuFDEQRS0EIpPAL0SWsmHTwa-23csoQIIUiQ2sLbfbPePBbXf8AOXv8TAhIBawKlXVuVXSvQBAjC4xpvztuI_rruy0X6LvCCKoo5gTwZ6BDWZctpEYnoMNQkh0GHN8Ak5z3reWcCxeghNKBoJ6ijbg_l3VvnNhrQWaGL5FX4uLQXsYbE0_S_ke01c4xwS3XlcTFw0np7chZpdhzS5sYV6tKY3uprZ1Aca1ONPEJu5sssFYWOISt0mvu4dX4MWsfbavH-sZ-PLh_efr2-7u083H66u7bmSYl45wTeXEZjJhhKTl84Q0M1bgkQ3UYI7kIIZJ095OEo_9yGdhERkl1sgIygw9A2-Od9cU76vNRS0uG-u9DjbWrAhjhB8cIg29-Avdx5qaCVnR5rdsYC_-RZFeMiz5QGmj5JEyKeac7KzW5BadHhRG6hCe-jM8dQhPHcNr0vPHB3Vc7PQk_JVWA9gRGJf97-f_vfsDBUGrvQ</recordid><startdate>20211101</startdate><enddate>20211101</enddate><creator>Sun, Sukkyu</creator><creator>Ha, Ahnul</creator><creator>Kim, Young Kook</creator><creator>Yoo, Byeong Wook</creator><creator>Kim, Hee Chan</creator><creator>Park, Ki Ho</creator><general>BMJ Publishing Group Ltd</general><general>BMJ Publishing Group LTD</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BTHHO</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-6037-8449</orcidid><orcidid>https://orcid.org/0000-0002-8137-8843</orcidid></search><sort><creationdate>20211101</creationdate><title>Dual-input convolutional neural network for glaucoma diagnosis using spectral-domain optical coherence tomography</title><author>Sun, Sukkyu ; Ha, Ahnul ; Kim, Young Kook ; Yoo, Byeong Wook ; Kim, Hee Chan ; Park, Ki Ho</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b416t-26a38d4f2d1008e6fd0a4ce71b493c1608979da35ed81b5b6f7e02b81a0c734c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Cardiovascular disease</topic><topic>Classification</topic><topic>Clinical science</topic><topic>Deep learning</topic><topic>Glaucoma</topic><topic>Glaucoma - diagnostic imaging</topic><topic>Glaucoma, Open-Angle - diagnostic imaging</topic><topic>Humans</topic><topic>Imaging</topic><topic>Intraocular Pressure</topic><topic>Macula</topic><topic>Medical diagnosis</topic><topic>Neural networks</topic><topic>Neural Networks, Computer</topic><topic>Optic Nerve</topic><topic>Optics</topic><topic>Retinal Ganglion Cells</topic><topic>ROC Curve</topic><topic>Tomography</topic><topic>Tomography, Optical Coherence</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sun, Sukkyu</creatorcontrib><creatorcontrib>Ha, Ahnul</creatorcontrib><creatorcontrib>Kim, Young Kook</creatorcontrib><creatorcontrib>Yoo, Byeong Wook</creatorcontrib><creatorcontrib>Kim, Hee Chan</creatorcontrib><creatorcontrib>Park, Ki Ho</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>BMJ Journals</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical 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><jtitle>British journal of ophthalmology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sun, Sukkyu</au><au>Ha, Ahnul</au><au>Kim, Young Kook</au><au>Yoo, Byeong Wook</au><au>Kim, Hee Chan</au><au>Park, Ki Ho</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dual-input convolutional neural network for glaucoma diagnosis using spectral-domain optical coherence tomography</atitle><jtitle>British journal of ophthalmology</jtitle><stitle>Br J Ophthalmol</stitle><addtitle>Br J Ophthalmol</addtitle><date>2021-11-01</date><risdate>2021</risdate><volume>105</volume><issue>11</issue><spage>1555</spage><epage>1560</epage><pages>1555-1560</pages><issn>0007-1161</issn><eissn>1468-2079</eissn><abstract>Background/AimsTo evaluate, with spectral-domain optical coherence tomography (SD-OCT), the glaucoma-diagnostic ability of a deep-learning classifier.MethodsA total of 777 Cirrus high-definition SD-OCT image sets of the retinal nerve fibre layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL) of 315 normal subjects, 219 patients with early-stage primary open-angle glaucoma (POAG) and 243 patients with moderate-to-severe-stage POAG were aggregated. The image sets were divided into a training data set (252 normal, 174 early POAG and 195 moderate-to-severe POAG) and a test data set (63 normal, 45 early POAG and 48 moderate-to-severe POAG). The visual geometry group (VGG16)-based dual-input convolutional neural network (DICNN) was adopted for the glaucoma diagnoses. Unlike other networks, the DICNN structure takes two images (both RNFL and GCIPL) as inputs. The glaucoma-diagnostic ability was computed according to both accuracy and area under the receiver operating characteristic curve (AUC).ResultsFor the test data set, DICNN could distinguish between patients with glaucoma and normal subjects accurately (accuracy=92.793%, AUC=0.957 (95% CI 0.943 to 0.966), sensitivity=0.896 (95% CI 0.896 to 0.917), specificity=0.952 (95% CI 0.921 to 0.952)). For distinguishing between patients with early-stage glaucoma and normal subjects, DICNN’s diagnostic ability (accuracy=85.185%, AUC=0.869 (95% CI 0.825 to 0.879), sensitivity=0.921 (95% CI 0.813 to 0.905), specificity=0.756 (95% CI 0.610 to 0.790)]) was higher than convolutional neural network algorithms that trained with RNFL or GCIPL separately.ConclusionThe deep-learning algorithm using SD-OCT can distinguish normal subjects not only from established patients with glaucoma but also from patients with early-stage glaucoma. The deep-learning model with DICNN, as trained by both RNFL and GCIPL thickness map data, showed a high diagnostic ability for discriminatingpatients with early-stage glaucoma from normal subjects.</abstract><cop>BMA House, Tavistock Square, London, WC1H 9JR</cop><pub>BMJ Publishing Group Ltd</pub><pmid>32920530</pmid><doi>10.1136/bjophthalmol-2020-316274</doi><tpages>6</tpages><orcidid>https://orcid.org/0000-0002-6037-8449</orcidid><orcidid>https://orcid.org/0000-0002-8137-8843</orcidid></addata></record> |
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subjects | Algorithms Cardiovascular disease Classification Clinical science Deep learning Glaucoma Glaucoma - diagnostic imaging Glaucoma, Open-Angle - diagnostic imaging Humans Imaging Intraocular Pressure Macula Medical diagnosis Neural networks Neural Networks, Computer Optic Nerve Optics Retinal Ganglion Cells ROC Curve Tomography Tomography, Optical Coherence |
title | Dual-input convolutional neural network for glaucoma diagnosis using spectral-domain optical coherence tomography |
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