Artificial intelligence–assisted detection of diabetic retinopathy on digital fundus images: concepts and applications in the National Health Service

Kouroupis, Michael, Korfiatis, Nikolaos and Cornford, James (2020) Artificial intelligence–assisted detection of diabetic retinopathy on digital fundus images: concepts and applications in the National Health Service. In: Innovation in Health Informatics. Elsevier, pp. 261-278. ISBN 9780128190432

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Abstract

Diabetic retinopathy (DR), one of the most devastating manifestations of diabetes, is a leading cause of blindness among working-age adults. World Health Organization predicts the prevalence of diabetes to increase substantially in the future, leading to an increasing pressure on public health services. In the context of smart healthcare, DR screening has been widely adopted by utilizing fundus imaging with manual input. In this chapter, we review the potential of artificial intelligence enabled automated screening for the detection and classification of DR in the context of the National Health Service. We propose an integrated multimodal approach to enable the combination of manual (through human graders) and automated DR screening. Furthermore, we discuss how this multimodal approach can be enhanced by the integration of additional modalities such as optical coherence tomography. Artificial intelligence in that setting can complement and upskill human graders by acting in an assistive way rather than replacing their role.

Item Type: Book Section
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
Faculty of Social Sciences > Norwich Business School
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Depositing User: LivePure Connector
Date Deposited: 27 Nov 2019 02:17
Last Modified: 23 May 2020 00:11
URI: https://ueaeprints.uea.ac.uk/id/eprint/73169
DOI: 10.1016/B978-0-12-819043-2.00011-3

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