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Research output: Contribution to Journal/Magazine › Review article › peer-review
Research output: Contribution to Journal/Magazine › Review article › peer-review
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TY - JOUR
T1 - Automatic detection of glaucoma via fundus imaging and artificial intelligence
T2 - A review
AU - Coan, Lauren J.
AU - Williams, Bryan M.
AU - Krishna Adithya, Venkatesh
AU - Upadhyaya, Swati
AU - Alkafri, Ala
AU - Czanner, Silvester
AU - Venkatesh, Rengaraj
AU - Willoughby, Colin E.
AU - Kavitha, Srinivasan
AU - Czanner, Gabriela
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Automatic detection
KW - Classification/discrimination
KW - Fundus images/imaging
KW - Glaucoma
KW - Prediction
KW - Segment/segmented/segmentation
U2 - 10.1016/j.survophthal.2022.08.005
DO - 10.1016/j.survophthal.2022.08.005
M3 - Review article
C2 - 35985360
AN - SCOPUS:85138216885
VL - 68
SP - 17
EP - 41
JO - Survey of Ophthalmology
JF - Survey of Ophthalmology
SN - 0039-6257
IS - 1
ER -