Automated diabetic retinopathy detection with two different retinal imaging devices using artificial intelligence: a com
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Automated diabetic retinopathy detection with two different retinal imaging devices using artificial intelligence: a comparison study Valentina Sarao 1,2 & Daniele Veritti 1 & Paolo Lanzetta 1,2 Received: 26 April 2020 / Revised: 12 June 2020 / Accepted: 14 July 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Purpose In this study, we evaluated the diagnostic performance of an automated artificial intelligence-based diabetic retinopathy (DR) algorithm with two retinal imaging systems using two different technologies: a conventional flash fundus camera and a white LED confocal scanner. Methods On the same day, patients underwent dilated colour fundus photography using both a conventional flash fundus camera (TRC-NW8, Topcon Corporation, Tokyo, Japan) and a fully automated white LED confocal scanner (Eidon, Centervue, Padova, Italy). All images were analysed for DR severity both by retina specialists and the AI software EyeArt (Eyenuk Inc., Los Angeles, CA) and graded as referable DR (RDR) or not RDR. Sensitivity, specificity and the area under the curve (AUC) were computed. Results A series of 165 diabetic subjects (330 eyes) were enrolled. The automated algorithm achieved 90.8% sensitivity with 75.3% specificity on images acquired with the conventional fundus camera and 94.1% sensitivity with 86.8% specificity on images obtained from the white LED confocal scanner. The difference between AUC was 0.0737 (p = 0.0023). Conclusion The automated image analysis software is well suited to work with different imaging technologies. It achieved a better diagnostic performance when the white LED confocal scanner is used. Further evaluation in the context of screening campaigns is needed. Keywords Artificial intelligence . Conventional flash fundus camera . Deep learning . Diabetic retinopathy . Telemedicine . White LED confocal scanner
Introduction Diabetic retinopathy (DR) is a primary complication of diabetes mellitus, and it is one of the main causes of blindness among working-age adults. With over 400 million people with diabetes worldwide, the overall prevalence of DR is 34.6%, with 10.2% of them having sight-threatening DR [1]. Early diagnosis of DR requires systematic and extensive screening due to the frequently asymptomatic nature of this major cause of preventable blindness [2]. Fundus photography has been accepted as the current mode of screening for DR with grading This article is part of a topical collection in Breakthroughs in deep learning for ophthalmology. * Paolo Lanzetta [email protected] 1
Department of Medicine—Ophthalmology, University of Udine, Via Colugna 50, 33100 Udine, Italy
2
Istituto Europeo di Microchirurgia Oculare-IEMO, Udine, Italy
of the fundus photographs being done by trained certified graders or retinal specialists [3]. With the exponential increase in the number of people with diabetes and shortage of trained graders/retinal specialists, an automated approach using artificial intelligence (AI) for DR screening has started attractin
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