Performance of an artificial intelligence automated system for diabetic eye screening in a large English population
Aims A diabetic eye screening programme has huge value in reducing avoidable sight loss by identifying diabetic retinopathy at a stage when it can be treated. Artificial intelligence automated systems can be used for diabetic eye screening but are not employed in the national English Diabetic Eye Sc...
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| Veröffentlicht in: | Diabetic medicine 2023-06, Vol.40 (6), p.e15055-n/a |
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| creator | Meredith, Sarah Grinsven, Mark Engelberts, Jonne Clarke, Dominic Prior, Vicki Vodrey, Jo Hammond, Alison Muhammed, Raja Kirby, Philip |
| description | Aims
A diabetic eye screening programme has huge value in reducing avoidable sight loss by identifying diabetic retinopathy at a stage when it can be treated. Artificial intelligence automated systems can be used for diabetic eye screening but are not employed in the national English Diabetic Eye Screening Programme. The aim was to report the performance of a commercially available deep‐learning artificial intelligence software in a large English population.
Methods
9817 anonymised image sets from 10,000 consecutive diabetic eye screening episodes were presented to an artificial intelligence software. The sensitivity and specificity of the artificial intelligence system for detecting diabetic retinopathy were determined using the diabetic eye screening programme manual grade according to national protocols as the reference standard.
Results
For no diabetic retinopathy versus any diabetic retinopathy, the sensitivity of the artificial intelligence grading system was 69.7% and specificity 92.2%. The performance of the artificial intelligence system was superior for no or mild diabetic retinopathy versus significant or referrable diabetic retinopathy with a sensitivity of 95.4% and specificity of 92.0%. No cases were identified in which the artificial intelligence grade had missed significant diabetic retinopathy.
Conclusion
The performance of a commercially available deep‐learning artificial intelligence system for identifying diabetic retinopathy in an English national Diabetic Eye Screening Programme is presented. Using the pre‐defined settings artificial intelligence performance was highest when identifying diabetic retinopathy which requires an action by the screening programme. |
| doi_str_mv | 10.1111/dme.15055 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2771332872</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2771332872</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3535-a17e243fae712e899dbac14599864b936c1a06cdacf352ad2d9418d2020c7ec03</originalsourceid><addsrcrecordid>eNp1kT1vFDEQhi0EIpdAwR9AlmhIsYm_d12icHxIQVBAbc3Zs4ejXfuwd4Xu3-NwgQKJaaaYZ17NvC8hLzi74q2uw4xXXDOtH5ENV0Z1Wln-mGxYr0QnWc_PyHmtd4xxYaV9Ss6k6bkVxmxI_YJlzGWG5JHmkUKiUJY4Rh9hojEtOE1xj_dTWJc8w4KB1mNdcKZtj4YIO1yip3hEWn1BTDHt2yIFOkHZI92m_RTrd3rIh3WCJeb0jDwZYar4_KFfkG_vtl9vPnS3n99_vHlz23mppe6A9yiUHAF7LnCwNuzAc6WtHYzaWWk8B2Z8AD9KLSCIYBUfgmCC-R49kxfk9Un3UPKPFevi5lh9ewgS5rU60fdcSjH0oqGv_kHv8lpSu86JoVmqjVG6UZcnypdca8HRHUqcoRwdZ-4-CdeScL-TaOzLB8V1N2P4S_6xvgHXJ-BnnPD4fyX39tP2JPkLwRmS_A</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2814656645</pqid></control><display><type>article</type><title>Performance of an artificial intelligence automated system for diabetic eye screening in a large English population</title><source>MEDLINE</source><source>Wiley Online Library</source><creator>Meredith, Sarah ; Grinsven, Mark ; Engelberts, Jonne ; Clarke, Dominic ; Prior, Vicki ; Vodrey, Jo ; Hammond, Alison ; Muhammed, Raja ; Kirby, Philip</creator><creatorcontrib>Meredith, Sarah ; Grinsven, Mark ; Engelberts, Jonne ; Clarke, Dominic ; Prior, Vicki ; Vodrey, Jo ; Hammond, Alison ; Muhammed, Raja ; Kirby, Philip</creatorcontrib><description>Aims
A diabetic eye screening programme has huge value in reducing avoidable sight loss by identifying diabetic retinopathy at a stage when it can be treated. Artificial intelligence automated systems can be used for diabetic eye screening but are not employed in the national English Diabetic Eye Screening Programme. The aim was to report the performance of a commercially available deep‐learning artificial intelligence software in a large English population.
Methods
9817 anonymised image sets from 10,000 consecutive diabetic eye screening episodes were presented to an artificial intelligence software. The sensitivity and specificity of the artificial intelligence system for detecting diabetic retinopathy were determined using the diabetic eye screening programme manual grade according to national protocols as the reference standard.
Results
For no diabetic retinopathy versus any diabetic retinopathy, the sensitivity of the artificial intelligence grading system was 69.7% and specificity 92.2%. The performance of the artificial intelligence system was superior for no or mild diabetic retinopathy versus significant or referrable diabetic retinopathy with a sensitivity of 95.4% and specificity of 92.0%. No cases were identified in which the artificial intelligence grade had missed significant diabetic retinopathy.
Conclusion
The performance of a commercially available deep‐learning artificial intelligence system for identifying diabetic retinopathy in an English national Diabetic Eye Screening Programme is presented. Using the pre‐defined settings artificial intelligence performance was highest when identifying diabetic retinopathy which requires an action by the screening programme.</description><identifier>ISSN: 0742-3071</identifier><identifier>EISSN: 1464-5491</identifier><identifier>DOI: 10.1111/dme.15055</identifier><identifier>PMID: 36719266</identifier><language>eng</language><publisher>England: Wiley Subscription Services, Inc</publisher><subject>Artificial Intelligence ; Automation ; Diabetes ; Diabetes Mellitus ; Diabetic retinopathy ; Diabetic Retinopathy - diagnosis ; Diabetic Retinopathy - epidemiology ; Eye ; Humans ; Mass Screening - methods ; Retinopathy ; Sensitivity and Specificity ; Software</subject><ispartof>Diabetic medicine, 2023-06, Vol.40 (6), p.e15055-n/a</ispartof><rights>2023 Diabetes UK.</rights><rights>Diabetic Medicine © 2023 Diabetes UK</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3535-a17e243fae712e899dbac14599864b936c1a06cdacf352ad2d9418d2020c7ec03</citedby><cites>FETCH-LOGICAL-c3535-a17e243fae712e899dbac14599864b936c1a06cdacf352ad2d9418d2020c7ec03</cites><orcidid>0000-0003-3563-468X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fdme.15055$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fdme.15055$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36719266$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Meredith, Sarah</creatorcontrib><creatorcontrib>Grinsven, Mark</creatorcontrib><creatorcontrib>Engelberts, Jonne</creatorcontrib><creatorcontrib>Clarke, Dominic</creatorcontrib><creatorcontrib>Prior, Vicki</creatorcontrib><creatorcontrib>Vodrey, Jo</creatorcontrib><creatorcontrib>Hammond, Alison</creatorcontrib><creatorcontrib>Muhammed, Raja</creatorcontrib><creatorcontrib>Kirby, Philip</creatorcontrib><title>Performance of an artificial intelligence automated system for diabetic eye screening in a large English population</title><title>Diabetic medicine</title><addtitle>Diabet Med</addtitle><description>Aims
A diabetic eye screening programme has huge value in reducing avoidable sight loss by identifying diabetic retinopathy at a stage when it can be treated. Artificial intelligence automated systems can be used for diabetic eye screening but are not employed in the national English Diabetic Eye Screening Programme. The aim was to report the performance of a commercially available deep‐learning artificial intelligence software in a large English population.
Methods
9817 anonymised image sets from 10,000 consecutive diabetic eye screening episodes were presented to an artificial intelligence software. The sensitivity and specificity of the artificial intelligence system for detecting diabetic retinopathy were determined using the diabetic eye screening programme manual grade according to national protocols as the reference standard.
Results
For no diabetic retinopathy versus any diabetic retinopathy, the sensitivity of the artificial intelligence grading system was 69.7% and specificity 92.2%. The performance of the artificial intelligence system was superior for no or mild diabetic retinopathy versus significant or referrable diabetic retinopathy with a sensitivity of 95.4% and specificity of 92.0%. No cases were identified in which the artificial intelligence grade had missed significant diabetic retinopathy.
Conclusion
The performance of a commercially available deep‐learning artificial intelligence system for identifying diabetic retinopathy in an English national Diabetic Eye Screening Programme is presented. Using the pre‐defined settings artificial intelligence performance was highest when identifying diabetic retinopathy which requires an action by the screening programme.</description><subject>Artificial Intelligence</subject><subject>Automation</subject><subject>Diabetes</subject><subject>Diabetes Mellitus</subject><subject>Diabetic retinopathy</subject><subject>Diabetic Retinopathy - diagnosis</subject><subject>Diabetic Retinopathy - epidemiology</subject><subject>Eye</subject><subject>Humans</subject><subject>Mass Screening - methods</subject><subject>Retinopathy</subject><subject>Sensitivity and Specificity</subject><subject>Software</subject><issn>0742-3071</issn><issn>1464-5491</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kT1vFDEQhi0EIpdAwR9AlmhIsYm_d12icHxIQVBAbc3Zs4ejXfuwd4Xu3-NwgQKJaaaYZ17NvC8hLzi74q2uw4xXXDOtH5ENV0Z1Wln-mGxYr0QnWc_PyHmtd4xxYaV9Ss6k6bkVxmxI_YJlzGWG5JHmkUKiUJY4Rh9hojEtOE1xj_dTWJc8w4KB1mNdcKZtj4YIO1yip3hEWn1BTDHt2yIFOkHZI92m_RTrd3rIh3WCJeb0jDwZYar4_KFfkG_vtl9vPnS3n99_vHlz23mppe6A9yiUHAF7LnCwNuzAc6WtHYzaWWk8B2Z8AD9KLSCIYBUfgmCC-R49kxfk9Un3UPKPFevi5lh9ewgS5rU60fdcSjH0oqGv_kHv8lpSu86JoVmqjVG6UZcnypdca8HRHUqcoRwdZ-4-CdeScL-TaOzLB8V1N2P4S_6xvgHXJ-BnnPD4fyX39tP2JPkLwRmS_A</recordid><startdate>202306</startdate><enddate>202306</enddate><creator>Meredith, Sarah</creator><creator>Grinsven, Mark</creator><creator>Engelberts, Jonne</creator><creator>Clarke, Dominic</creator><creator>Prior, Vicki</creator><creator>Vodrey, Jo</creator><creator>Hammond, Alison</creator><creator>Muhammed, Raja</creator><creator>Kirby, Philip</creator><general>Wiley Subscription Services, Inc</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>7T5</scope><scope>8FD</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-3563-468X</orcidid></search><sort><creationdate>202306</creationdate><title>Performance of an artificial intelligence automated system for diabetic eye screening in a large English population</title><author>Meredith, Sarah ; Grinsven, Mark ; Engelberts, Jonne ; Clarke, Dominic ; Prior, Vicki ; Vodrey, Jo ; Hammond, Alison ; Muhammed, Raja ; Kirby, Philip</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3535-a17e243fae712e899dbac14599864b936c1a06cdacf352ad2d9418d2020c7ec03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial Intelligence</topic><topic>Automation</topic><topic>Diabetes</topic><topic>Diabetes Mellitus</topic><topic>Diabetic retinopathy</topic><topic>Diabetic Retinopathy - diagnosis</topic><topic>Diabetic Retinopathy - epidemiology</topic><topic>Eye</topic><topic>Humans</topic><topic>Mass Screening - methods</topic><topic>Retinopathy</topic><topic>Sensitivity and Specificity</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Meredith, Sarah</creatorcontrib><creatorcontrib>Grinsven, Mark</creatorcontrib><creatorcontrib>Engelberts, Jonne</creatorcontrib><creatorcontrib>Clarke, Dominic</creatorcontrib><creatorcontrib>Prior, Vicki</creatorcontrib><creatorcontrib>Vodrey, Jo</creatorcontrib><creatorcontrib>Hammond, Alison</creatorcontrib><creatorcontrib>Muhammed, Raja</creatorcontrib><creatorcontrib>Kirby, Philip</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Immunology Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Diabetic medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Meredith, Sarah</au><au>Grinsven, Mark</au><au>Engelberts, Jonne</au><au>Clarke, Dominic</au><au>Prior, Vicki</au><au>Vodrey, Jo</au><au>Hammond, Alison</au><au>Muhammed, Raja</au><au>Kirby, Philip</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Performance of an artificial intelligence automated system for diabetic eye screening in a large English population</atitle><jtitle>Diabetic medicine</jtitle><addtitle>Diabet Med</addtitle><date>2023-06</date><risdate>2023</risdate><volume>40</volume><issue>6</issue><spage>e15055</spage><epage>n/a</epage><pages>e15055-n/a</pages><issn>0742-3071</issn><eissn>1464-5491</eissn><abstract>Aims
A diabetic eye screening programme has huge value in reducing avoidable sight loss by identifying diabetic retinopathy at a stage when it can be treated. Artificial intelligence automated systems can be used for diabetic eye screening but are not employed in the national English Diabetic Eye Screening Programme. The aim was to report the performance of a commercially available deep‐learning artificial intelligence software in a large English population.
Methods
9817 anonymised image sets from 10,000 consecutive diabetic eye screening episodes were presented to an artificial intelligence software. The sensitivity and specificity of the artificial intelligence system for detecting diabetic retinopathy were determined using the diabetic eye screening programme manual grade according to national protocols as the reference standard.
Results
For no diabetic retinopathy versus any diabetic retinopathy, the sensitivity of the artificial intelligence grading system was 69.7% and specificity 92.2%. The performance of the artificial intelligence system was superior for no or mild diabetic retinopathy versus significant or referrable diabetic retinopathy with a sensitivity of 95.4% and specificity of 92.0%. No cases were identified in which the artificial intelligence grade had missed significant diabetic retinopathy.
Conclusion
The performance of a commercially available deep‐learning artificial intelligence system for identifying diabetic retinopathy in an English national Diabetic Eye Screening Programme is presented. Using the pre‐defined settings artificial intelligence performance was highest when identifying diabetic retinopathy which requires an action by the screening programme.</abstract><cop>England</cop><pub>Wiley Subscription Services, Inc</pub><pmid>36719266</pmid><doi>10.1111/dme.15055</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-3563-468X</orcidid></addata></record> |
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| source | MEDLINE; Wiley Online Library |
| subjects | Artificial Intelligence Automation Diabetes Diabetes Mellitus Diabetic retinopathy Diabetic Retinopathy - diagnosis Diabetic Retinopathy - epidemiology Eye Humans Mass Screening - methods Retinopathy Sensitivity and Specificity Software |
| title | Performance of an artificial intelligence automated system for diabetic eye screening in a large English population |
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