diaendoСахарный диабетDiabetes mellitus2072-03512072-0378Endocrinology research centre10.14341/DM13354diaendo-13354Research ArticleОбзорыReviewОбзор систем поддержки принятия врачебных решений для диагностики диабетической ретинопатииReview of medical decision support systems for the diagnosis of diabetic retinopathyhttps://orcid.org/0000-0002-9462-8522Першина-МилютинаА. П.Pershina-MiliutinaA. P.

Першина-Милютина Анастасия Павловна  

117036, Москва, ул. Дм. Ульянова, д. 11 

Anastasiia P. Pershina-Miliutina, MD 

11 Dm.Ulyanova street, 117036 Moscow 

oa11111998@gmail.comhttps://orcid.org/0009-0008-2206-1505КозловЕ. В.KozlovE. V.

Козлов Егор Васильевич  

Москва 

Egor V. Kozlov

Moscow 

kozlov.egor@endocrincentr.ruhttps://orcid.org/0009-0008-2158-2624ЛысухинД. Д.LysukhinD. D.

Лысухин Даниил Дмитриевич 

Москва 

Daniil D. Lysukhin 

Moscow 

lysukhin.daniil@endocrincentr.ruhttps://orcid.org/0009-0007-1336-4152АредовА. В.Aredov A. V.

Аредов Алексей Вячеславович  

Москва 

Aleksey V. Aredov 

Moscow 

aredov.aleksey@endocrincentr.ruhttps://orcid.org/0000-0002-9258-2591КовалеваЕ. В.KovalevaE. V.

Ковалева Елена Владимировна 

Москва 

Elena V. Kovaleva, MD, PhD 

Moscow 

kovaleva.elena@endocrincentr.ruhttps://orcid.org/0000-0002-9717-9742МокрышеваН. Г.MokryshevaN. G.

Мокрышева Наталья Георгиевна, д.м.н., профессор, академик РАН 

Москва 

Researcher ID: AAY-3761-2020

Scopus Author ID: 35269746000 

Natalya G. Mokrysheva, MD, PhD, Professor, Academician of the RAS 

Moscow 

Researcher ID: AAY-3761-2020

Scopus Author ID: 35269746000 

mokrisheva.natalia@endocrincentr.ruГНЦ РФ ФГБУ «Национальный медицинский исследовательский центр эндокринологии им. академика И.И. Дедова»РоссияEndocrinology Research CentreRussian Federation202503122025285460470Copyright © Першина-Милютина А.П., Козлов Е.В., Лысухин Д.Д., Аредов А.В., Ковалева Е.В., Мокрышева Н.Г., 20252025Першина-Милютина А.П., Козлов Е.В., Лысухин Д.Д., Аредов А.В., Ковалева Е.В., Мокрышева Н.Г.Pershina-Miliutina A.P., Kozlov E.V., Lysukhin D.D., Aredov  A.V., Kovaleva E.V., Mokrysheva N.G.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.dia-endojournals.ru/jour/article/view/13354

Диабетическая ретинопатия (ДР) является одним из наиболее распространённых и опасных осложнений сахарного диабета, часто приводящим к слепоте. Учитывая высокую распространённость заболевания и ограниченные ресурсы офтальмологической службы, особую актуальность приобретает внедрение автоматизированных систем для раннего выявления и мониторинга ДР. Настоящий обзор посвящен анализу современных решений в области применения методов компьютерного зрения и искусственного интеллекта (ИИ) для диагностики ДР. Проведён систематический поиск с последующим анализом 31 работы, описывающих различные подходы, включая использование сверточных нейронных сетей, методов сегментации патологий глазного дна, гибридных алгоритмов и мобильных приложений для скрининга. Обсуждаются ключевые характеристики, архитектуры, чувствительность и специфичность предложенных моделей, а также применяемые датасеты. Особое внимание уделено внедрённым в клиническую практику решениям, таким как IDx-DR и EyeArt, одобренным регулирующими органами. Подчеркивается значимость интерпретируемости моделей, разнообразия обучающих данных и стандартизации изображений как критически важных факторов для повышения обобщающей способности и доверия к ИИ-системам в офтальмологии. Отличительной особенностью данной работы является всесторонний охват как международных, так и отечественных разработок, включая оценку перспектив их интеграции в систему здравоохранения России. Обзор подводит к выводу о зрелости ряда технологий, пригодных для клинического применения, при этом подчёркивая необходимость дальнейших исследований, направленных на повышение точности, устойчивости и прозрачности алгоритмов диагностики.

Diabetic retinopathy (DR) is one of the most common and severe complications of diabetes mellitus, often leading to blindness. Given the high prevalence of the disease and the limited capacity of ophthalmology services, the implementation of automated systems for early detection and monitoring of DR is becoming increasingly important. This review focuses on current advances in the application of computer vision and artificial intelligence (AI) techniques for DR diagnosis. A systematic search was conducted, followed by an analysis of 31 studies describing various approaches, including convolutional neural networks, pathology segmentation methods for fundus images, hybrid algorithms, and mobile applications for screening. Key features, model architectures, sensitivity and specificity metrics, as well as datasets used, are discussed. Special attention is given to AI systems already integrated into clinical practice, such as IDx-DR and EyeArt, which have received regulatory approval. The review highlights the importance of model interpretability, training data diversity, and image standardization as critical factors for improving the generalizability and trustworthiness of AI systems in ophthalmology. A distinctive aspect of this work is its comprehensive coverage of both international and Russian developments, including an assessment of their integration potential within the Russian healthcare system. The review concludes that several technologies have reached a level of maturity suitable for clinical use, while emphasizing the need for further research to enhance the accuracy, robustness, and transparency of diagnostic algorithms.

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