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Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review.

Coan, L, Williams, B, Venkatesh, KA, Upadhyaya, S, Kafri, AA, Czanner, S, Venkatesh, R, Willoughby, CE, Kavitha, S and Czanner, G (2022) Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review. Survey of ophthalmology. ISSN 0039-6257

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Open Access URL: https://doi.org/10.1016/j.survophthal.2022.08.005 (Published version)


Glaucoma is a leading cause of irreversible vision impairment globally, and cases are continuously rising worldwide. Early detection is crucial, allowing timely intervention that can prevent further visual field loss. To detect glaucoma, examination of the optic nerve head via fundus imaging can be performed, at the center of which is the assessment of the optic cup and disc boundaries. Fundus imaging is non-invasive and low-cost; however, the image examination relies on subjective, time-consuming, and costly expert assessments. A timely question to ask is: "Can artificial intelligence mimic glaucoma assessments made by experts?". Specifically, can artificial intelligence automatically find the boundaries of the optic cup and disc (providing a so-called segmented fundus image) and then use the segmented image to identify glaucoma with high accuracy? We conducted a comprehensive review on artificial intelligence-enabled glaucoma detection frameworks that produce and use segmented fundus images and summarized the advantages and disadvantages of such frameworks. We identified 36 relevant papers from 2011-2021 and 2 main approaches: 1) logical rule-based frameworks, based on a set of rules; and 2) machine learning/statistical modelling based frameworks. We critically evaluated the state-of-art of the 2 approaches, identified gaps in the literature and pointed at areas for future research.

Item Type: Article
Uncontrolled Keywords: artificial intelligence; automatic detection; classification/discrimination; fundus images/imaging; glaucoma; prediction; segment/segmented/segmentation; 1113 Opthalmology and Optometry; Ophthalmology & Optometry
Subjects: R Medicine > RE Ophthalmology
Divisions: Computer Science & Mathematics
Publisher: Elsevier BV
SWORD Depositor: A Symplectic
Date Deposited: 01 Sep 2022 14:24
Last Modified: 29 Nov 2022 13:00
DOI or ID number: 10.1016/j.survophthal.2022.08.005
URI: https://researchonline.ljmu.ac.uk/id/eprint/17478
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