Application of artificial intelligence in glaucoma. Part 1. Neural networks and deep learning in glaucoma screening and diagnosis

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This article reviews literature on the use of artificial intelligence (AI) for screening, diagnosis, monitoring and treatment of glaucoma. The first part of the review provides information how AI methods improve the effectiveness of glaucoma screening, presents the technologies using deep learning, including neural networks, for the analysis of big data obtained by methods of ocular imaging (fundus imaging, optical coherence tomography of the anterior and posterior eye segments, digital gonioscopy, ultrasound biomicroscopy, etc.), including a multimodal approach. The results found in the reviewed literature are contradictory, indicating that improvement of the AI models requires further research and a standardized approach. The use of neural networks for timely detection of glaucoma based on multimodal imaging will reduce the risk of blindness associated with glaucoma.

Keywords:

artificial intelligence

convolutional neural networks

glaucoma

optical coherence tomography

Authors:

Kurysheva N.I.

Medical Biological University of Innovations and Continuing Education of the Federal Biophysical Center named after A.I. Burnazyan;
Ophthalmological Center of the Federal Medical-Biological Agency at the Federal Biophysical Center named after A.I. Burnazyan

SPIN RSCI: 3531-5880
Scopus AuthorID: 6603842519
ORCID: 0000-0002-2265-6671

Rodionova O.Ye.

N.N. Semenov Federal Research Center for Chemical Physics

ORCID: 0000-0002-0146-8284

Pomerantsev A.L.

N.N. Semenov Federal Research Center for Chemical Physics

ORCID: 0000-0001-7402-4011

Sharova G.A.

Medical Biological University of Innovations and Continuing Education of the Federal Biophysical Center named after A.I. Burnazyan;
OOO Glaznaya Klinika Doktora Belikovoy

SPIN RSCI: 7067-0326
Scopus AuthorID: 57220597890
ORCID: 0000-0002-7163-4858

Received:

24.01.2024

Accepted:

01.04.2024

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