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dc.contributor.authorStorås, Andrea Marheim
dc.contributor.authorStrumke, Inga
dc.contributor.authorRiegler, Michael Alexander
dc.contributor.authorGrauslund, Jakob
dc.contributor.authorHammer, Hugo Lewi
dc.contributor.authorYazidi, Anis
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorGundersen, Kjell Gunnar
dc.contributor.authorUtheim, Tor Paaske
dc.contributor.authorJackson, Catherine Joan
dc.identifier.citationThe ocular surface. 2022, 23 74-86.en_US
dc.description.abstractDry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term ‘AI’ is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.en_US
dc.relation.ispartofseriesThe ocular surface;Volume 23, January 2022
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.subjectDry eye diseaseen_US
dc.subjectArtificial intelligenceen_US
dc.subjectMachine learningen_US
dc.titleArtificial intelligence in dry eye diseaseen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.source.journalThe ocular surfaceen_US

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal