{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:52:31Z","timestamp":1790833951767,"version":"4.1.0"},"reference-count":32,"publisher":"SAGE Publications","issue":"11","license":[{"start":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T00:00:00Z","timestamp":1571616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T00:00:00Z","timestamp":1571616000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Technology &amp; Therapeutics"],"published-print":{"date-parts":[[2019,11]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Current manual diabetic retinopathy (DR) screening using eye care experts cannot scale to screen the growing population of diabetes patients who are at risk for vision loss. EyeArt system is an automated, cloud-based artificial intelligence (AI) eye screening technology designed to easily detect referral-warranted DR immediately through automated analysis of patient's retinal images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>This retrospective study assessed the diagnostic efficacy of the EyeArt system v2.0 analyzing 850,908 fundus images from 101,710 consecutive patient visits, collected from 404 primary care clinics. Presence or absence of referral-warranted DR (more than mild nonproliferative DR [NPDR]) was automatically detected by the EyeArt system for each patient encounter, and its performance was compared against a clinical reference standard of quality-assured grading by rigorously trained certified ophthalmologists and optometrists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Of the 101,710 visits, 75.7% were nonreferable, 19.3% were referable to an eye care specialist, and in 5.0%, the DR level was unknown as per the clinical reference standard. EyeArt screening had 91.3% (95% confidence interval [CI]: 90.9\u201391.7) sensitivity and 91.1% (95% CI: 90.9\u201391.3) specificity. For 5446 encounters with potentially treatable DR (more than moderate NPDR and\/or diabetic macular edema), the system provided a positive \u201crefer\u201d output to 5363 encounters achieving sensitivity of 98.5%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>This study captures variations in real-world clinical practice and shows that an AI DR screening system can be safe and effective in the real world. This study demonstrates the value of this easy-to-use, automated tool for endocrinologists, diabetologists, and general practitioners to address the growing need for DR screening and monitoring.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1089\/dia.2019.0164","type":"journal-article","created":{"date-parts":[[2019,7,23]],"date-time":"2019-07-23T16:11:14Z","timestamp":1563898274000},"page":"635-643","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":195,"title":["The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with Diabetes"],"prefix":"10.1177","volume":"21","author":[{"given":"Malavika","family":"Bhaskaranand","sequence":"first","affiliation":[{"name":"Eyenuk, Inc., Los Angeles, California."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaithanya","family":"Ramachandra","sequence":"additional","affiliation":[{"name":"Eyenuk, Inc., Los Angeles, California."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandeep","family":"Bhat","sequence":"additional","affiliation":[{"name":"Eyenuk, Inc., Los Angeles, California."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorge","family":"Cuadros","sequence":"additional","affiliation":[{"name":"EyePACS LLC, San Jose, California."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muneeswar G.","family":"Nittala","sequence":"additional","affiliation":[{"name":"Doheny Eye Institute, Los Angeles, California."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Srinivas R.","family":"Sadda","sequence":"additional","affiliation":[{"name":"Doheny Eye Institute, Los Angeles, California."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaushal","family":"Solanki","sequence":"additional","affiliation":[{"name":"Eyenuk, Inc., Los Angeles, California."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2019,10,21]]},"reference":[{"key":"e_1_3_4_2_2","unstructured":"CDC Diabetes Report Card: 2017. www.cdc.gov\/diabetes\/library\/reports\/congress.html (accessed November26 2018)."},{"key":"e_1_3_4_3_2","unstructured":"Center for Disease Control: National Diabetes Statistics Report 2017. https:\/\/www.cdc.gov\/diabetes\/pdfs\/data\/statistics\/national-diabetes-statistics-report.pdf (accessed January20 2019)."},{"key":"e_1_3_4_4_2","unstructured":"World Health Organization: Diabetes. 2018. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/diabetes (accessed January20 2019)."},{"key":"e_1_3_4_5_2","doi-asserted-by":"crossref","unstructured":"National Eye Institute: Facts about diabetic eye disease. 2015. https:\/\/nei.nih.gov\/health\/diabetic\/retinopathy (accessed November25 2018).","DOI":"10.1007\/978-3-319-08329-2_3"},{"key":"e_1_3_4_6_2","unstructured":"Fox CR Kronenberg K Chu G et al.: Increasing eye care screening & referral for people with diabetes via telehealth programs. 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