Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone.
Sign Up to like & getrecommendations! Published in 2019 at "JAMA ophthalmology"
DOI: 10.1001/jamaophthalmol.2019.2923
Abstract: Importance Offline automated analysis of retinal images on a smartphone may be a cost-effective and scalable method of screening for diabetic retinopathy; however, to our knowledge, assessment of such an artificial intelligence (AI) system is… read more here.
Keywords: retinopathy; referable diabetic; system; diabetic retinopathy ... See more keywords
Automated multidimensional deep learning platform for referable diabetic retinopathy detection: a multicentre, retrospective study
Sign Up to like & getrecommendations! Published in 2022 at "BMJ Open"
DOI: 10.1136/bmjopen-2021-060155
Abstract: Objective To develop and validate a real-world screening, guideline-based deep learning (DL) system for referable diabetic retinopathy (DR) detection. Design This is a multicentre platform development study based on retrospective, cross-sectional data sets. Images were… read more here.
Keywords: referable diabetic; retinopathy detection; detection; deep learning ... See more keywords
Validation of Automated Screening for Referable Diabetic Retinopathy With an Autonomous Diagnostic Artificial Intelligence System in a Spanish Population
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Diabetes Science and Technology"
DOI: 10.1177/1932296820906212
Abstract: Purpose: The purpose of this study is to compare the diagnostic performance of an autonomous artificial intelligence (AI) system for the diagnosis of referable diabetic retinopathy (RDR) to manual grading by Spanish ophthalmologists. Methods: Subjects… read more here.
Keywords: referable diabetic; diabetic retinopathy; system; autonomous diagnostic ... See more keywords