{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T04:38:59Z","timestamp":1793162339884,"version":"4.5.0"},"reference-count":17,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"Suppl 1","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose:<\/jats:title>\n                    <jats:p>To quantifiably assess the diagnostic accuracy of Adven-I, a proprietary artificial intelligence (AI)-driven diagnostic system that automatically detects diseases from fundus images. The purpose is to quantify the performance of Adven-i in differentiating a nonreferable (within normal limits) image from a referable (diseased fundus) image and further segregating diabetic retinopathy (DR) from the rest of the abnormalities (non-DR) encompassing the wide spectrum of abnormal pathologies. The assessment is carried out in comparison to manual reading as the reference gold standard. Adven-i is the only AI system classifying retinal abnormalities into DR and non-DR classes separately, apart from predicting nonreferable fundus, while most existing systems classify fundus images into referable and nonreferable DR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>The double-blinded study was conducted on retrospective data collected over the course of a year in the ophthalmology outpatient department (OPD) at a top Tier II eyecare hospital in Chandigarh, India. Three vitreoretina specialists who were blinded to one another read the images. The ground-truth was generated on the basis of majority agreement among the readers. An arbitrator's decision was regarded final if all three readers disagreed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>2261 fundus images were analyzed by Adven-i. The sensitivity and specificity of Adven-i in diagnosing images with abnormalities were 95.12% and 85.77%, respectively, and for segregating DR from rest of the retinal abnormalities were 91.87% and 85.12%, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions and Relevance:<\/jats:title>\n                    <jats:p>Adven-i shows definite promise in automated screening for early diagnosis of referable fundus images including DR. Adven-i can be adopted to scale for mass screening in resource-limited settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/ijo.ijo_3342_22","type":"journal-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T01:03:27Z","timestamp":1703207007000},"page":"S46-S52","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":4,"title":["A double-blinded study for quantifiable assessment of the diagnostic accuracy of AI tool \u201cADVEN-i\u201d in identifying diseased fundus images including diabetic retinopathy on a retrospective data"],"prefix":"10.4103","volume":"72","author":[{"given":"Mausumi","family":"Acharyya","sequence":"first","affiliation":[{"name":"R&D, Advenio, Drishti Eye Hospital, Panchkula, Haryana, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bruttendu","family":"Moharana","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Drishti Eye Hospital, Panchkula, Haryana, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sahil","family":"Jain","sequence":"additional","affiliation":[{"name":"Department of Vitreo-retina Services, Mirchia Laser Eye Clinic, Chandigarh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manjari","family":"Tandon","sequence":"additional","affiliation":[{"name":"Department of Retina and Uvea Services, Mirchia Laser Eye Clinic, Chandigarh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2023,12,22]]},"reference":[{"key":"R1-20260508","doi-asserted-by":"crossref","first-page":"e144","DOI":"10.1016\/S2214-109X(20)30489-7","article-title":"Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: The Right to Sight: An analysis for the Global Burden of Disease Study","volume":"9","year":"2021","journal-title":"Lancet Glob Health"},{"key":"R2-20260508","doi-asserted-by":"crossref","first-page":"e0271736","DOI":"10.1371\/journal.pone.0271736","article-title":"Blindness and visual impairment and their causes in India: Results of a nationally representative survey","volume":"17","author":"Vashist","year":"2022","journal-title":"PLoS One"},{"key":"R3-20260508"},{"key":"R4-20260508","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1177\/014107680309600604","article-title":"Screening for diabetic retinopathy","volume":"96","author":"Squirrell","year":"2003","journal-title":"J R Soc Med"},{"key":"R5-20260508","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1136\/jech.52.1.45","article-title":"Impact of diabetic retinopathy screening on a British district population: Case detection and blindness prevention in an evidence-based model","volume":"52","author":"Bachmann","year":"1998","journal-title":"J Epidemiol Community Health"},{"key":"R6-20260508","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/s00592-017-0974-1","article-title":"The English national screening programme for diabetic retinopathy 2003-2016","volume":"54","author":"Scanlon","year":"2017","journal-title":"Acta Diabetol"},{"key":"R7-20260508","doi-asserted-by":"crossref","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"},{"key":"R8-20260508","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"R9-20260508","doi-asserted-by":"crossref","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 (London)"},{"key":"R10-20260508","doi-asserted-by":"crossref","first-page":"1677","DOI":"10.1016\/S0161-6420(03)00475-5","article-title":"Global Diabetic Retinopathy Project Group. 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