{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T16:38:40Z","timestamp":1776357520636,"version":"3.51.2"},"reference-count":21,"publisher":"S. Karger AG","issue":"3","license":[{"start":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T00:00:00Z","timestamp":1603929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/karger.com\/pages\/terms-and-conditions"},{"start":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T00:00:00Z","timestamp":1603929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/karger.com\/pages\/terms-and-conditions"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Ophthalmologica"],"published-print":{"date-parts":[[2021]]},"abstract":"<jats:p>Purpose: To evaluate the diagnostic accuracy of a diagnostic system software for the automated screening of diabetic retinopathy (DR) on digital colour fundus photographs, the 2019 Convolutional Neural Network (CNN) model with Inception-V3. Methods: In this cross-sectional study, 295 fundus images were analysed by the CNN model and compared to a panel of ophthalmologists. Images were obtained from a dataset acquired within a screening programme. Diagnostic accuracy measures and respective 95% CI were calculated. Results: The sensitivity and specificity of the CNN model in diagnosing referable DR was 81% (95% CI 66\u201390%) and 97% (95% CI 95\u201399%), respectively. Positive predictive value was 86% (95% CI 72\u201394%) and negative predictive value 96% (95% CI 93\u201398%). The positive likelihood ratio was 33 (95% CI 15\u201375) and the negative was 0.20 (95% CI 0.11\u20130.35). Its clinical impact is demonstrated by the change observed in the pre-test probability of referable DR (assuming a prevalence of 16%) to a post-test probability for a positive test result of 86% and for a negative test result of 4%. Conclusion: A CNN model negative test result safely excludes DR, and its use may significantly reduce the burden of ophthalmologists at reading centres. <\/jats:p>","DOI":"10.1159\/000512638","type":"journal-article","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T22:00:31Z","timestamp":1604008831000},"page":"250-257","source":"Crossref","is-referenced-by-count":31,"title":["Screening for Diabetic Retinopathy Using an Automated Diagnostic System Based on Deep Learning: Diagnostic Accuracy Assessment"],"prefix":"10.1159","volume":"244","author":[{"given":"S\u00edlvia","family":"R\u00eago","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0421-1211","authenticated-orcid":false,"given":"Marco","family":"Dutra-Medeiros","sequence":"additional","affiliation":[]},{"given":"Filipe","family":"Soares","sequence":"additional","affiliation":[]},{"given":"Matilde","family":"Monteiro-Soares","sequence":"additional","affiliation":[]}],"member":"127","published-online":{"date-parts":[[2020,10,29]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-85900-2"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.4103\/0974-9233.154391"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2005.11.021"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-31744-1_33"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.2337\/dc11-1909"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2013.11.002"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s41433-018-0324-8"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1136\/bjophthalmol-2018-313173"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.7860\/JCDR\/2016\/18129.8744"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.2147\/SHTT.S64448"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.4239\/wjd.v4.i6.290"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s00592-017-0974-1"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/s41433-018-0269-y"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105320"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2019.0164"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-018-0040-6"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1167\/iovs.17-23677"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.08.079"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1136\/bjophthalmol-2017-311489"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1167\/tvst.9.2.35"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.4103\/0301-4738.49397"}],"container-title":["Ophthalmologica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/karger.com\/oph\/article-pdf\/244\/3\/250\/3909075\/000512638.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/karger.com\/oph\/article-pdf\/244\/3\/250\/3909075\/000512638.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T16:05:04Z","timestamp":1745424304000},"score":1,"resource":{"primary":{"URL":"https:\/\/karger.com\/article\/doi\/10.1159\/000512638"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,29]]},"references-count":21,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,7,1]]},"published-print":{"date-parts":[[2021,7,6]]}},"URL":"https:\/\/doi.org\/10.1159\/000512638","archive":["Portico"],"relation":{},"ISSN":["0030-3755","1423-0267"],"issn-type":[{"value":"0030-3755","type":"print"},{"value":"1423-0267","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,29]]}}}