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Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification

Camara, J. ; Neto, A. ; Pires, I.M.P. ; Villasana, M. ; Zdravevski, E. ; Cunha, A.

journal of imaging Vol. 8, Nº 2, pp. 19 - 19, January, 2022.

ISSN (print): 2313-433X
ISSN (online): 2313-433X

Scimago Journal Ranking: 0,73 (in 2021)

Digital Object Identifier: 10.3390/jimaging8020019

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Abstract
Artificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer aided diagnosis. The combination
of computer science methods and medical knowledge facilitates and improves the accuracy of the different processes and tools. Inspired by these advances, this paper performs a literature review focused on state-of-the-art glaucoma screening, segmentation, and classification based on images of
the papilla and excavation using deep learning techniques. These techniques have been shown to have high sensitivity and specificity in glaucoma screening based on papilla and excavation images.
The automatic segmentation of the contours of the optic disc and the excavation then allows the identification and assessment of the glaucomatous disease’s progression. As a result, we verified whether deep learning techniques may be helpful in performing accurate and low-cost measurements
related to glaucoma, which may promote patient empowerment and help medical doctors better monitor patients.