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

Kouroupis, Michael, Korfiatis, Nikolaos ORCID: https://orcid.org/0000-0001-6377-4837 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
Uncontrolled Keywords:	sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School Faculty of Social Sciences > Norwich Business School
UEA Research Groups:	Faculty of Social Sciences > Research Groups > Innovation, Technology and Operations Management Faculty of Social Sciences > Research Centres > Centre for Competition Policy Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023)
Related URLs:	https://linkinghub.elsevier.com/retrieve...
Depositing User:	LivePure Connector
Date Deposited:	27 Nov 2019 02:17
Last Modified:	21 Apr 2023 01:45
URI:	https://ueaeprints.uea.ac.uk/id/eprint/73169
DOI:	10.1016/B978-0-12-819043-2.00011-3

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