Journal article
Artificial intelligence algorithms to diagnose glaucoma and detect glaucoma progression: Translation to clinical practice
AS Mursch-Edlmayr, WS Ng, A Diniz-Filho, DC Sousa, L Arnould, MB Schlenker, K Duenas-Angeles, PA Keane, JG Crowston, H Jayaram
Translational Vision Science and Technology | ASSOC RESEARCH VISION OPHTHALMOLOGY INC | Published : 2020
DOI: 10.1167/tvst.9.2.55
Open access
Abstract
Purpose: This concise review aims to explore the potential for the clinical implemen-tation of artificial intelligence (AI) strategies for detecting glaucoma and monitoring glaucoma progression. Methods: Nonsystematic literature review using the search combinations “Artifi-cial Intelligence,” “Deep Learning,” “Machine Learning,” “Neural Networks,” “Bayesian Networks,” “Glaucoma Diagnosis,” and “Glaucoma Progression.” Information on sensitivity and specificity regarding glaucoma diagnosis and progression analysis as well as methodological details were extracted. Results: Numerous AI strategies provide promising levels of specificity and sensitivity for structural (e.g. optical coherence tomog..
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Funding Acknowledgements
A.S.M.-E., W.S.N., A.D.-F., D.C.S., L.A., M.B.S., K.D.-A., J.G.C., and H.J. are participants in an independent educational program ("International Glaucoma Panel") supported by the Allergan Medical Institute. P.A.K. is supported by a Moorfields Eye Charity Career Development Award and a UK Research and Innovation Future Leaders Fellowship. H.J. is supported by the Moorfields Eye Charity. P.A.K. and H.J. are grateful for the support of the National Institute for Health Research Biomedical Research Centre for Ophthalmology atMoorfields Eye Hospital and the UCL Institute of Ophthalmology. The views expressed in this paper are those of the authors and not necessarily those of any funding body or the UK Department of Health.