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Kurysheva N.I.

Medical Biological University of Innovations and Continuing Education of the Burnazyan Federal Biophysical Center;
Ophthalmological Center of Federal Medical-Biological Agency of Russia

Pomerantsev A.L.

Federal Research Center for Chemical Physics of the Russian Academy of Sciences

Rodionova O.Ye.

Federal Research Center for Chemical Physics of the Russian Academy of Sciences

Sharova G.A.

Medical Biological University of Innovations and Continuing Education of the Burnazyan Federal Biophysical Center;
OOO Glaznaya klinika doktora Belikovoy

Application of artificial intelligence methods in the diagnosis and treatment of primary angle-closure disease

Authors:

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

More about the authors

Journal: Russian Annals of Ophthalmology. 2024;140(5): 130‑136

DOI: 10.17116/oftalma2024140051130

Read: 1711 times

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Table of contents

APPLICATION OF ARTIFICIAL INTELLIGENCE METHODS IN THE DIAGNOSIS AND TREATMENT OF PRIMARY ANGLE-CLOSURE DISEASE
APPLICATION OF ARTIFICIAL INTELLIGENCE METHODS IN THE DIAGNOSIS AND TREATMENT OF PRIMARY ANGLE-CLOSURE DISEASE

To cite this article:

Kurysheva NI, Pomerantsev AL, Rodionova OYe, Sharova GA. Application of artificial intelligence methods in the diagnosis and treatment of primary angle-closure disease. Russian Annals of Ophthalmology. 2024;140(5):130‑136. (In Russ.)
https://doi.org/10.17116/oftalma2024140051130

Подписка 2026 Вестник офтальмологии (Russian Annals of Ophthalmology)
МСФ на ЯМ
Использование инфографики авторами научных работ
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Abstract:

This article reviews literature on the use of artificial intelligence (AI) methods for the diagnosis and treatment of primary angle-closure disease (PACD). The review describes how AI techniques enhance the efficiency of population screening for anterior chamber angle closure, presents technologies utilizing deep learning, including neural networks, for the analysis of large datasets obtained through anterior segment imaging methods, such as anterior segment optical coherence tomography (AS-OCT), digital gonioscopy, and ultrasound biomicroscopy, and discusses methods for treating PACD with the help of AI. Integration of deep learning and imaging techniques represents a crucial step in optimizing the diagnosis and treatment of PACD, reducing the burden on the healthcare system.

Keywords:

artificial intelligence (AI)
data driven soft independent modelling of class analogies (DD-SIMCA)
primary angle closure disease
anterior segment optical coherence tomography (AS-OCT)
digital gonioscopy

Authors:

Kurysheva N.I.

Medical Biological University of Innovations and Continuing Education of the Burnazyan Federal Biophysical Center;
Ophthalmological Center of Federal Medical-Biological Agency of Russia

Pomerantsev A.L.

Federal Research Center for Chemical Physics of the Russian Academy of Sciences

Rodionova O.Ye.

Federal Research Center for Chemical Physics of the Russian Academy of Sciences

Sharova G.A.

Medical Biological University of Innovations and Continuing Education of the Burnazyan Federal Biophysical Center;
OOO Glaznaya klinika doktora Belikovoy

Received:

22.02.2024

Accepted:

26.03.2024

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References:

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