Thumbnail Image
Publication

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

Coan, Lauren J.
; 
Williiams, Bryan M.
; 
Krishna Adithya, Venkatesh
; 
Upadhyaya, Swati
; 
Alkafri, Ala
; 
Czanner, Silvester
; 
Venkatesh, Rengaraj
; 
Willoughby, Colin E.
; 
Kavitha, Srinivasan
; 
Czanner, Gabriela
Advisors
Editors
Other Contributors
Affiliation
Liverpool John Moores University; Lancaster University; Aravind Eye Hospital; Teesside University; Slovak University of Technology; Ulster University
EPub Date
Publication Date
2022-08-17
Submitted Date
Collections
Computer Science
Show all metadata
Files
Thumbnail Image
Article - VoR
Adobe PDF, 1.74 MB
Other Titles
Abstract
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 an 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 noninvasive and low-cost; however, 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 to 2021 and 2 main approaches: 1) logical rule-based frameworks, based on a set of rules; and 2) machine learning/statistical modeling-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.
Citation
Coan, L. J., Williiams, B. M., Krishna Adithya, V., Upadhyaya, S., Alkafri, A., Czanner, S., Venkatesh, R., Willoughby, C. E., Kavitha, S., & Czanner, G. (2023). Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review. Survey of Ophthalmology, 68(1), 17-41. https://doi.org/10.1016/j.survophthal.2022.08.005
Publisher
Elsevier
Journal
Survey of Ophthalmology
Research Unit
URI
http://hdl.handle.net/10034/629359
DOI
10.1016/j.survophthal.2022.08.005
PubMed ID
PubMed Central ID
Type
Article
Language
en
Description
© 2022 The Authors. Published by Elsevier Inc.
Series/Report no.
ISSN
0039-6257
EISSN
1879-3304
ISBN
ISMN
Gov't Doc
Test Link
Sponsors
This work was supported by a PhD studentship funding from the British Council for Prevention of Blindness
Additional Links
https://www.sciencedirect.com/science/article/pii/S0039625722001163?via%3Dihub