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Review
. 2024 Mar 1;14(5):530.
doi: 10.3390/diagnostics14050530.

Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging

Affiliations
Review

Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging

Clerimar Paulo Bragança et al. Diagnostics (Basel). .

Abstract

The progress of artificial intelligence algorithms in digital image processing and automatic diagnosis studies of the eye disease glaucoma has been growing and presenting essential advances to guarantee better clinical care for the population. Given the context, this article describes the main types of glaucoma, traditional forms of diagnosis, and presents the global epidemiology of the disease. Furthermore, it explores how studies using artificial intelligence algorithms have been investigated as possible tools to aid in the early diagnosis of this pathology through population screening. Therefore, the related work section presents the main studies and methodologies used in the automatic classification of glaucoma from digital fundus images and artificial intelligence algorithms, as well as the main databases containing images labeled for glaucoma and publicly available for the training of machine learning algorithms.

Keywords: artificial intelligence; deep learning; glaucoma; image analysis.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
ISNT (Inferior (I), Superior (S), Nasal (N) and Temporal (T)) Rule.
Figure 2
Figure 2
Measures considered in the CDR calculation.
Figure 3
Figure 3
Example of CDR calculation with figure showing excavation of 0.6.
Figure 4
Figure 4
Example of image with segmentation of the disc and optical cup.

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