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Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration
Abstract Purpose To validate the performance of a commercially available, CE-certified deep learning (DL) system, RetCAD v.1.3.0 (Thirona, Nijmegen, The Netherlands), for the joint automatic detection of diabetic retinopathy (DR) and age-related macular degeneration (AMD) in colour fundus (CF) images on a dataset with mixed presence of eye diseases. Methods Evaluation of joint detection of […]
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Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs
Abstract Background/Objectives Artificial intelligence can assist with ocular image analysis for screening and diagnosis, but it is not yet capable of autonomous full-spectrum screening. Hypothetically, false-positive results may have unrealized screening potential arising from signals persisting despite training and/or ambiguous signals such as from biomarker overlap or high comorbidity. The study aimed to explore the […]
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Performance of a deep learning system for detection of referable diabetic retinopathy in real clinical settings
Abstract Background: To determine the ability of a commercially available deep learning system, RetCAD v.1.3.1 (Thirona, Nijmegen, The Netherlands) for the automatic detection of referable diabetic retinopathy (DR) on a dataset of colour fundus images acquired during routine clinical practice in a tertiary hospital screening program, analyzing the reduction of workload that can be released […]
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Simultaneous screening and classification of diabetic retinopathy and age-related macular degeneration based on fundus photos—a prospective analysis of the RetCAD system
Abstract AIM To assess the accuracy of an artificial intelligence (AI) based software (RetCAD, Thirona, The Netherlands) to identify and grade age-related macular degeneration (AMD) and diabetic retinopathy (DR) simultaneously based on fundus photos. METHODS This prospective study included 1245 eyes of 630 patients attending an ophthalmology day-care clinic. Fundus photos were acquired and parallel […]
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Performance of an artificial intelligence automated system for diabetic eye screening in a large English population
Abstract Aims A diabetic eye screening programme has huge value in reducing avoidable sight loss by identifying diabetic retinopathy at a stage when it can be treated. Artificial intelligence automated systems can be used for diabetic eye screening but are not employed in the national English Diabetic Eye Screening Programme. The aim was to report […]
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Diabetic retinopathy screening with confocal fundus camera and artificial intelligence - assisted grading
Purpose Screening for diabetic retinopathy (DR) by ophthalmologists is costly and labour-intensive. Artificial Intelligence (AI) for automated DR detection could be a clinically and economically alternative. We assessed the performance of a confocal fundus imaging system (DRSplus, Centervue SpA), coupled with an AI algorithm (RetCAD, Thirona B.V.) in a real-world setting. Methods 45° non-mydriatic retinal […]
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Diabetic Retinopathy Screening with Artificial Intelligence Preliminary experience in Italian Healthcare System
Purpose: New technologies, such as non- mydriatic cameras and artificial intelligence (AI) software, allow to recognize elementary lesions, making a first diagnosis of retinopathy, reducing the diagnostic burden on eye care specialists and time costs for patients. We present a preliminary report of the performance of artificial intelligence RetCAD ™ ( Thirona Retina BV Nijmegen, […]
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