Journal article

Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice.

Anna S Mursch-Edlmayr, Wai Siene Ng, Alberto Diniz-Filho, David C Sousa, Louis Arnold, Matthew B Schlenker, Karla Duenas-Angeles, Pearse A Keane, Jonathan G Crowston, Hari Jayaram

Translational Vision Science and Technology | Published : 2020

Abstract

Purpose: This concise review aims to explore the potential for the clinical implementation of artificial intelligence (AI) strategies for detecting glaucoma and monitoring glaucoma progression. Methods: Nonsystematic literature review using the search combinations "Artificial 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 tomogra..

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Grants

Awarded by Department of Health


Awarded by Medical Research Council