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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

Rodionova O.Ye.

N.N. Semenov Federal Research Center for Chemical Physics

Pomerantsev A.L.

N.N. Semenov Federal Research Center for Chemical Physics

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

Application of artificial intelligence in glaucoma. Part 2. Neural networks and machine learning in the monitoring and treatment of glaucoma

Authors:

Kurysheva N.I., Rodionova O.Ye., Pomerantsev A.L., Sharova G.A.

More about the authors

Journal: Russian Annals of Ophthalmology. 2024;140(4): 80‑85

DOI: 10.17116/oftalma202414004180

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APPLICATION OF ARTIFICIAL INTELLIGENCE IN GLAUCOMA. PART 2. NEURAL NETWORKS AND MACHINE LEARNING IN THE MONITORING AND TREATMENT OF GLAUCOMA
APPLICATION OF ARTIFICIAL INTELLIGENCE IN GLAUCOMA. PART 2. NEURAL NETWORKS AND MACHINE LEARNING IN THE MONITORING AND TREATMENT OF GLAUCOMA

To cite this article:

Kurysheva NI, Rodionova OYe, Pomerantsev AL, Sharova GA. Application of artificial intelligence in glaucoma. Part 2. Neural networks and machine learning in the monitoring and treatment of glaucoma. Russian Annals of Ophthalmology. 2024;140(4):80‑85. (In Russ.)
https://doi.org/10.17116/oftalma202414004180

Подписка 2025-2 (Russian Annals of Ophthalmology)
 
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The second part of the literature review on the application of artificial intelligence (AI) methods for screening, diagnosing, monitoring, and treating glaucoma provides information on how AI methods enhance the effectiveness of glaucoma monitoring and treatment, presents technologies that use machine learning, including neural networks, to predict disease progression and determine the need for anti-glaucoma surgery. The article also discusses the methods of personalized treatment based on projection machine learning methods and outlines the problems and prospects of using AI in solving tasks related to screening, diagnosing, and treating glaucoma.

Keywords:

artificial intelligence
machine learning
convolutional neural networks
deep learning
DD-SIMCA method
glaucoma
anterior segment 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

Rodionova O.Ye.

N.N. Semenov Federal Research Center for Chemical Physics

Pomerantsev A.L.

N.N. Semenov Federal Research Center for Chemical Physics

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

Received:

24.01.2024

Accepted:

01.04.2024

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