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• W4379142714 abstract "<sec> <title>BACKGROUND</title> The use of Artificial intelligence (AI) algorithms for detecting different ophthalmic diseases, especially diabetic retinopathy (DR), has become increasingly popular. </sec> <sec> <title>OBJECTIVE</title> To evaluate the screening performance of different AI algorithms based on convolutional neural networks (CNNs) in a real-world scenario </sec> <sec> <title>METHODS</title> Observational and cross-sectional study conducted on patients aged ≥18 years with type-2 diabetes mellitus, who had undergone fundus examination for DR screening using a teleophthalmology program between May and August 2021 in four Catalan primary care centers. Color fundus photos were acquired, using a non-mydriatic camera, and transmitted to the reading center of the ophthalmological clinic. We used the UPRETINA diagnostic system, which consists of 8 AI algorithms based on CNN's. Sensitivity and specificity; inter- and intra-observer agreement; and area under the receiver operating characteristic curve (AUROC) were calculated. </sec> <sec> <title>RESULTS</title> A total of 1652 eyes from 871 patients [1652 images] were analyzed. The AI algorithms had a sensitivity/specificity of 86.8%/95.6% for detecting DR; 94.9%/94.3% for detecting age-related macular degeneration (AMD); 82.7%/92.4% for detecting glaucomatous optic neuropathy (GON); 87.0%/87.5% for detecting epiretinal membrane; and 89.7%/98.0% for detecting nevus. Additionally, the sensitivity/specificity for correctly classify images as right eye/left eye and to correctly classify images gradeability (medium or high quality) were 100% /100% and 92.9%/90.5%, respectively. The AUROC of the AI algorithms ranged between 0.9777 (AMD) and 0.9122 (GON). </sec> <sec> <title>CONCLUSIONS</title> UPRETINA system algorithms were capable to automatically and accurately classify the screening retinographies, which obviously have a significant impact on reducing workload. In addition, it has been shown to be able to distinguish pathological images from non-pathological ones, leading to a scenario of more efficient optimization of resources, in which retina specialists will be focused on assessing only pathological images. This would also reduce primary-care burden. The incorporation of a greater spectrum of data, always maintaining and guaranteeing patient confidentiality, will help to establish a patient-tailored health care. </sec> <sec> <title>CLINICALTRIAL</title> ClinicalTRials. Gov NCT04132401. </sec> <sec> <title>INTERNATIONAL REGISTERED REPORT</title> RR2-10.2196/12539 </sec>" @default.
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• W4379142714 date "2023-05-26" @default.
• W4379142714 modified "2023-09-25" @default.
• W4379142714 title "Clinical validation of an artificial intelligence algorithms for the detection of different central-involved retinal pathologies and glaucoma by using a non-mydriatic camera (Preprint)" @default.
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• W4379142714 doi "https://doi.org/10.2196/preprints.49389" @default.
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