glaucomaНациональный журнал ГлаукомаNational Journal glaucoma2078-41042311-6862Federal State Budgetary Institution of Science “Krasnov Research Institute of Eye Diseases”10.53432/2078-4104-2023-22-1-115-128glaucoma-441Research ArticleОБЗОРЫ ЛИТЕРАТУРЫLITERATURE REVIEWSИскусственный интеллект и нейросети в диагностике глаукомыArtificial intelligence and neural networks in the diagnosis of glaucomaДорофеевД. А.DorofeevD. A.

Дорофеев Дмитрий Александрович - врач-офтальмолог, руководитель городского глаукомного кабинета.

454090, Российская Федерация, Челябинск, ул. Российская, 200

Ophthalmologist, Head of the Glaucoma Office.

200 Rossiyskaya St., Chelyabinsk, Russian Federation, 454090

dimmm.83@gmail.comКазановаС. ЮKazanovaS. Yu.

Заведующая офтальмологическим консультативно-диагностическим отделением №1 (глаукомное отделение).

150040, Российская Федерация, Ярославль, пр. Октября, 52

Head of the Consultative-Diagnostic Department No. 1 (Glaucoma Department).

52 Oktyabrya St., Yaroslavl, Russian Federation, 150040

МовсисянА. Б.MovsisyanA. B.

Врач-офтальмолог; аспирант, ассистент кафедры офтальмологии.

109472, Российская Федерация, Москва, Волгоградский пр-т, 168

107014, Российская Федерация, Москва, ул. Большая Оленья, 8a

Ophthalmologist; postgraduate student, Assistant Professor at the Academic Department of Ophthalmology.

Moscow, 168 Volgogradskiy Pr., Moscow, Russian Federation, 109472

8a Bolshaya Olenya St., Moscow, Russian Federation, 107014

ПолеваР. П.PolevaR. P.

К.м.н., старший научный сотрудник отдела современных методов лечения в офтальмологии.

119021, Российская Федерация, Москва, ул. Россолимо, 11А, Б.

Cand. Sci. (Med.), senior researcher at the Department of Modern Treatment Methods in Ophthalmology.

11A Rossolimo St., Moscow, Russian Federation, 119021

ГАУЗ ГКБ No2, поликлиника №1РоссияMunicipal Clinical Hospital No. 2, Polyclinic No. 1Russian FederationГБКУЗ ЯО «Центральная городская больница»РоссияCentral City HospitalRussian FederationГБУЗ «Госпиталь для ветеранов войн №2 ДЗМ»; ФКУ «ЦВКГ им. П.В. Мандрыка» Минобороны РоссииРоссияHospital for War Veterans No. 2; P.V. Mandryka Central Military Clinical HospitalRussian FederationФГБНУ «НИИГБ им. М.М. Краснова»РоссияKrasnov Research Institute of Eye DiseasesRussian Federation202323032023221115128Copyright © Дорофеев Д.А., Казанова С.Ю., Мовсисян А.Б., Полева Р.П., 20232023Дорофеев Д.А., Казанова С.Ю., Мовсисян А.Б., Полева Р.П.Dorofeev D.A., Kazanova S.Y., Movsisyan A.B., Poleva R.P.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.glaucomajournal.ru/jour/article/view/441

Одной из важнейших проблем офтальмологии является ранняя диагностика глаукомы и объективный анализ данных инструментальных исследований. Современный этап развития технологий позволяет применить возможности искусственного интеллекта и нейросетей в диагностике и лечении глаукомы. Так, специальное программное обеспечение позволяет выполнять периметрию с помощью портативных устройств, что снижает нагрузку на лечебные учреждения и удешевляет исследование. Математические модели позволяют оценить риск прогрессирования заболевания на основе данных исследований. Искусственный интеллект позволяет оценить результат выполнения тонометрии по Гольдману и Маклакову и определить наличие прогрессирования по серии как двумерных, так и трехмерных данных (сканы диска зрительного нерва, статическая периметрия и т.д.) как поодиночке, так и при комплексной оценке данных с различных приборов.

Early diagnosis of glaucoma and objective analysis of data obtained from instrumental study methods is one of the most important problems in ophthalmology. Modern state of technological development allows implementing artificial intelligence and neural networks in the diagnosis and treatment of glaucoma. Special software helps perform perimetry using portable devices, which reduces the workload for medical facilities and lowers the costs of the procedure. Mathematical models allow evaluating the risk of glaucoma progression based on instrumental findings. Artificial intelligence allows assessing the results of Goldman and Maklakov tonometry and determining the state of disease progression by analyzing a series of 2D and 3D data (scan images of optic nerve head, static perimetry etc.) separately, as well as in complex analysis of data from various devices.

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The authors declare that there are no conflicts of interest present.