{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T08:25:16Z","timestamp":1785918316601,"version":"3.56.0"},"reference-count":28,"publisher":"Association for Research in Vision and Ophthalmology (ARVO)","issue":"2","license":[{"start":{"date-parts":[[2020,12,4]],"date-time":"2020-12-04T00:00:00Z","timestamp":1607040000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Trans. Vis. Sci. Tech."],"published-print":{"date-parts":[[2020,12,4]]},"DOI":"10.1167\/tvst.9.2.60","type":"journal-article","created":{"date-parts":[[2020,12,5]],"date-time":"2020-12-05T06:47:16Z","timestamp":1607150836000},"page":"60","source":"Crossref","is-referenced-by-count":33,"title":["Automatic Identification of Referral-Warranted Diabetic Retinopathy Using Deep Learning on Mobile Phone Images"],"prefix":"10.1167","volume":"9","author":[{"given":"Cassie A.","family":"Ludwig","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA, USA"},{"name":"Retina Service, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chandrashan","family":"Perera","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Myung","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA, USA"},{"name":"Department of Chemical Engineering, Stanford University, Stanford, CA, USA"},{"name":"VA Palo Alto Health Care System, Palo Alto, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Margaret A.","family":"Greven","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Wake Forest Baptist Health, Winston Salem, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen J.","family":"Smith","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA, USA"},{"name":"VA Palo Alto Health Care System, Palo Alto, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert T.","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Theodore","family":"Leng","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"27","reference":[{"key":"bib1","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.diabres.2017.03.024","article-title":"IDF Diabetes Atlas: global estimates for the prevalence of diabetes for 2015 and 2040","volume":"128","author":"Ogurtsova","year":"2017","journal-title":"Diabetes Res Clin Pract"},{"key":"bib3","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.diabres.2018.02.023","article-title":"IDF Diabetes Atlas: global estimates of diabetes prevalence for 2017 and projections for 2045","volume":"138","author":"Cho","year":"2018","journal-title":"Diabetes Res Clin Pract"},{"issue":"6","key":"bib5","doi-asserted-by":"publisher","first-page":"e339","DOI":"10.1016\/S2214-109X(13)70113-X","article-title":"Causes of vision loss worldwide, 1990-2010: a systematic analysis","volume":"1","author":"Bourne","year":"2013","journal-title":"Lancet Glob Health"},{"issue":"11","key":"bib6","doi-asserted-by":"publisher","first-page":"e0206742","DOI":"10.1371\/journal.pone.0206742","article-title":"Factors influencing patient adherence with diabetic eye screening in rural communities: A qualitative study","volume":"13","author":"Liu","year":"2018","journal-title":"PLoS One"},{"key":"bib7","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1186\/1748-5908-9-87","article-title":"Printed educational messages aimed at family practitioners fail to increase retinal screening among their patients with diabetes: a pragmatic cluster randomized controlled trial [ISRCTN72772651]","volume":"9","author":"Zwarenstein","year":"2014","journal-title":"Implement Sci"},{"issue":"22","key":"bib8","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"issue":"7","key":"bib9","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1016\/j.ophtha.2017.02.008","article-title":"Automated identification of diabetic retinopathy using deep learning","volume":"124","author":"Gargeya","year":"2017","journal-title":"Ophthalmology"},{"key":"bib10","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","article-title":"Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning","volume":"2","author":"Poplin","year":"2018","journal-title":"Nat Biomed Engineering"},{"issue":"1","key":"bib11","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","article-title":"Scalable and accurate deep learning with electronic health records","volume":"1","author":"Rajkomar","year":"2018","journal-title":"npj Digit Med"},{"issue":"7","key":"bib12","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1001\/jamaophthalmol.2018.1934","article-title":"Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks","volume":"136","author":"Brown","year":"2018","journal-title":"JAMA Ophthalmol"},{"issue":"8","key":"bib14","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1016\/j.compmedimag.2009.06.003","article-title":"Algorithms for digital image processing in diabetic retinopathy","volume":"33","author":"Winder","year":"2009","journal-title":"Comput Med Imaging Graph"},{"issue":"12","key":"bib15","doi-asserted-by":"publisher","first-page":"2136","DOI":"10.1016\/j.compbiomed.2013.10.007","article-title":"Computer-aided diagnosis of diabetic retinopathy: a review","volume":"43","author":"Mookiah","year":"2013","journal-title":"Comput Biol Med"},{"issue":"3","key":"bib16","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int J Comput Vis"},{"issue":"3","key":"bib19","doi-asserted-by":"publisher","first-page":"231","DOI":"10.5566\/ias.1155","article-title":"Feedback on a publicly distributed image database: the Messidor database","volume":"33","author":"Decenci\u00e8re","year":"2014","journal-title":"Image Anal Stereol"},{"issue":"3","key":"bib20","doi-asserted-by":"publisher","first-page":"191","DOI":"10.4103\/0301-4738.181742","article-title":"A novel smartphone ophthalmic imaging adapter: user feasibility studies in Hyderabad, India","volume":"64","author":"Ludwig","year":"2016","journal-title":"Indian J Ophthalmol"},{"issue":"5","key":"bib21","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1097\/IAE.0000000000000955","article-title":"Smartphone-based dilated fundus photography and near visual acuity testing as inexpensive screening tools to detect referral warranted diabetic eye disease","volume":"36","author":"Toy","year":"2016","journal-title":"Retina"},{"issue":"5","key":"bib22","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1109\/JBHI.2013.2294635","article-title":"DREAM: diabetic retinopathy analysis using machine learning","volume":"18","author":"Roychowdhury","year":"2014","journal-title":"IEEE J Biomed Health Inform"},{"issue":"4","key":"bib23","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1109\/TMI.2015.2509785","article-title":"Red lesion detection using dynamic shape features for diabetic retinopathy screening","volume":"35","author":"Seoud","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"issue":"6","key":"bib24","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1038\/s41433-018-0064-9","article-title":"Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence","volume":"32","author":"Rajalakshmi","year":"2018","journal-title":"Eye (Lond)"},{"issue":"6","key":"bib25","doi-asserted-by":"publisher","first-page":"631","DOI":"10.3928\/23258160-20150610-06","article-title":"Quality and diagnostic utility of mydriatic smartphone photography: the Smartphone Ophthalmoscopy Reliability Trial","volume":"46","author":"Adam","year":"2015","journal-title":"Ophthalmic Surg Lasers Imaging Retina"},{"key":"bib26","first-page":"55958","article-title":"Smartphone fundus photography","volume":"125","author":"Nazari Khanamiri","year":"2017","journal-title":"J Vis Exp"},{"issue":"13","key":"bib27","doi-asserted-by":"publisher","first-page":"5200","DOI":"10.1167\/iovs.16-19964","article-title":"Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning","volume":"57","author":"Abramoff","year":"2016","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"22","key":"bib28","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"bib2","volume-title":"Global Report on Diabetes","author":"Organization WH","year":"2018"},{"key":"bib4","volume-title":"Diabetes atlas","author":"Federation ID","year":"2015"},{"key":"bib13","volume-title":"Social Media Use Continues to Rise in Developing Countries but Plateaus Across Developed Ones","author":"Poushter","year":"2018"},{"key":"bib17","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2017.243","article-title":"Densely connected convolutional networks","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Huang","year":"2017"},{"key":"bib18","volume-title":"A Disciplined approach to Neural Network Hyper-parameters: Part 1 \u2013 Learning Rate Batch Size, Momentum, and Weight Decay","author":"Smith","year":"2018"}],"container-title":["Translational Vision Science &amp; Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/tvst.arvojournals.org\/article.aspx?articleid=2772036","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,5]],"date-time":"2020-12-05T06:47:18Z","timestamp":1607150838000},"score":1,"resource":{"primary":{"URL":"https:\/\/tvst.arvojournals.org\/article.aspx?articleid=2772036"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,4]]},"references-count":28,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,1,28]]}},"URL":"https:\/\/doi.org\/10.1167\/tvst.9.2.60","relation":{},"ISSN":["2164-2591"],"issn-type":[{"value":"2164-2591","type":"print"}],"subject":[],"published":{"date-parts":[[2020,12,4]]}}}