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dc.contributor.authorFineide, Fredrik
dc.contributor.authorChen, Xiangjun
dc.contributor.authorMagnø, Morten Schjerven
dc.contributor.authorYazidi, Anis
dc.contributor.authorRiegler, Michael
dc.contributor.authorUtheim, Tor Paaske
dc.contributor.authorStorås, Andrea Marheim
dc.date.accessioned2022-12-20T08:58:38Z
dc.date.available2022-12-20T08:58:38Z
dc.date.issued2022-12-10
dc.description.abstractDry eye disease is one of the most common ophthalmological complaints and is defined by a loss of tear film homeostasis. Establishing a diagnosis can be time-consuming, resource demanding and unpleasant for the patient. In this pilot study, we retrospectively included clinical data from 431 patients with dry eye disease examined in the Norwegian Dry Eye Clinic to evaluate how artificial intelligence algorithms perform on clinical data related to dry eye disease. The data was processed and subjected to numerous machine learning classification algorithms with the aim to predict decreased tear film break-up time. Moreover, feature selection techniques (information gain and information gain ratio) were applied to determine which clinical factors contribute most to an unstable tear film. The applied machine learning algorithms outperformed baseline classifications performed with ZeroR according to included evaluation metrics. Clinical features such as ocular surface staining, meibomian gland expressibility and dropout, blink frequency, osmolarity, meibum quality and symptom score were recognized as important predictors for tear film instability. We identify and discuss potential limitations and pitfalls.en_US
dc.identifier.citationFineide F, Storås A, Chen X, Magnø MS, Yazidi A, Riegler M, Utheim TP. Predicting an unstable tear film through artificial intelligence. Scientific Reports. 2022;12(1)en_US
dc.identifier.cristinIDFRIDAID 2092452
dc.identifier.doihttps://doi.org/10.1038/s41598-022-25821-y
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/10037/27891
dc.language.isoengen_US
dc.publisherNature Researchen_US
dc.relation.journalScientific Reports
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titlePredicting an unstable tear film through artificial intelligenceen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)