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

Coan, Lauren J. and Williams, Bryan M. and Krishna Adithya, Venkatesh and Upadhyaya, Swati and Alkafri, Ala and Czanner, Silvester and Venkatesh, Rengaraj and Willoughby, Colin E. and Kavitha, Srinivasan and Czanner, Gabriela (2023) Automatic detection of glaucoma via fundus imaging and artificial intelligence : A review. Survey of Ophthalmology, 68 (1). pp. 17-41. ISSN 0039-6257

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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.

Item Type:
Journal Article
Journal or Publication Title:
Survey of Ophthalmology
Uncontrolled Keywords:
Research Output Funding/no_not_funded
Subjects:
?? segmentationno - not fundednoophthalmology ??
ID Code:
184721
Deposited By:
Deposited On:
01 Feb 2023 16:55
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Jul 2024 23:28