{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Nunez do Rio JM"],"funding":["UK Research and Innovation"],"pagination":["1392"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9876892"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(1)"],"pubmed_abstract":["Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existing automated algorithms have mostly been developed from images acquired with high cost mydriatic retinal cameras and cannot be applied in the settings used in most low- and middle-income countries. In this prospective multicentre study, we developed a deep learning system (DLS) that detects referable DR from retinal images acquired using handheld non-mydriatic fundus camera by non-technical field workers in 20 sites across India. Macula-centred and optic-disc-centred images from 16,247 eyes (9778 participants) were used to train and cross-validate the DLS and risk factor based logistic regression models. The DLS achieved an AUROC of 0.99 (100"],"journal":["Scientific reports"],"pubmed_title":["Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings."],"pmcid":["PMC9876892"],"funding_grant_id":["MR/P027881/1"],"pubmed_authors":["Manayath G","Agarwal M","Bergeles C","Das S","Anantharaman G","Nunez do Rio JM","Sil AK","Rajalakshmi R","SMART India Study Group","Rani PK","Roy R","Gopalakrishnan M","Behera U","Nderitu P","Sen A","Natarajan S","Gopal L","Naigaonkar R","Surya J","Bhattacharjee H","Sivaprasad S","Jitesh R","Bhende P","Kulkarni S","Mani SL","Cherian T","Raman R","Krishnan R","Kim R","Barman M","Chakabarty S","Desai A","Ramakrishnan R","Vignesh TP","Saxena M"],"additional_accession":[]},"is_claimable":false,"name":"Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings.","description":"Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existing automated algorithms have mostly been developed from images acquired with high cost mydriatic retinal cameras and cannot be applied in the settings used in most low- and middle-income countries. In this prospective multicentre study, we developed a deep learning system (DLS) that detects referable DR from retinal images acquired using handheld non-mydriatic fundus camera by non-technical field workers in 20 sites across India. Macula-centred and optic-disc-centred images from 16,247 eyes (9778 participants) were used to train and cross-validate the DLS and risk factor based logistic regression models. The DLS achieved an AUROC of 0.99 (100","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2025-04-04T19:48:07.344Z","creation":"2025-04-04T19:48:07.344Z"},"accession":"S-EPMC9876892","cross_references":{"pubmed":["36697482"],"doi":["10.1038/s41598-023-28347-z"]}}