Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age‐related macular degeneration

Author:

González‐Gonzalo Cristina1234ORCID,Sánchez‐Gutiérrez Verónica5,Hernández‐Martínez Paula5,Contreras Inés56ORCID,Lechanteur Yara T.4,Domanian Artin4,Ginneken Bram2,Sánchez Clara I.1234

Affiliation:

1. A‐eye Research Group Radboud University Medical Center Nijmegen The Netherlands

2. Diagnostic Image Analysis Group Radboud University Medical Center Nijmegen The Netherlands

3. Donders Institute for Brain Cognition and Behaviour Radboud University Medical Center Nijmegen The Netherlands

4. Department of Ophthalmology Radboud University Medical Center Nijmegen The Netherlands

5. Department of Ophthalmology University Hospital Ramón y Cajal Ramón y Cajal Health Research Institute (IRYCIS) Madrid Spain

6. Clínica Rementería Madrid Spain

Publisher

Wiley

Subject

Ophthalmology,General Medicine

Reference67 articles.

1. AAO(2015):PPP Retina/Vitreous Panel Hoskins Center for Quality Eye Care. Preferred practice pattern: age‐related macular degeneration – Updated 2015. Available at:https://www.aao.org/preferred-practice-pattern/age-related-macular-degeneration-ppp-2015. (Accessed on 4 March 2019).

2. AAO(2017):Retina/Vitreous Panel Hoskins Center for Quality Eye Care. Preferred practice pattern: diabetic retinopathy ‐ Updated 2017. Available at:https://www.aao.org/preferred-practice-pattern/diabetic-retinopathy-ppp-updated-2017. (Accessed on 4 March 2019).

3. Automated Analysis of Retinal Images for Detection of Referable Diabetic Retinopathy

4. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning

5. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices

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