diaendoСахарный диабетDiabetes mellitus2072-03512072-0378Endocrinology research centre10.14341/DM12665diaendo-12665Research ArticleОбзорыReviewИскусственный интеллект в диабетологииArtificial intelligence in diabetologyhttps://orcid.org/0000-0002-5407-8722КлимонтовВ. В.KlimontovV. V.

Климонтов Вадим Валерьевич - доктор медицинских наук; eLibrary SPIN: 1734-4030.

630060, Новосибирск, ул. Тимакова, д.2

Vadim V. Klimontov, MD, PhD, Dr. Med. Sci. ; eLibrary SPIN: 1734-4030.

2, Timakov Str., Novosibirsk, 630060

klimontov@mail.ruhttps://orcid.org/0000-0002-5207-9764БериковВ. Б.BerikovV. B.

Бериков Владимир Борисович – доктор технических наук, ведущий научный сотрудник, eLibrary SPIN: 8108-2591.

Новосибирск

Vladimir B. Berikov, PhD in. Tech. Sci., senior research associate, eLibrary SPIN: 8108-2591.

Novosibirsk

berikov@math.nsc.ruhttps://orcid.org/0000-0003-1880-1300СайкО. В.SaikO. V.

Сайк Ольга Владимировна - кандидат биологических наук, научный сотрудник; eLibrary SPIN: 6702-1490.

Новосибирск

Olga V. Saik, PhD in Biology, research associate; eLibrary SPIN: 6702-1490.

Novosibirsk

saik@bionet.nsc.ruФедеральный исследовательский центр Институт цитологии и генетики, Сибирское отделение Российской академии наукРоссияFederal Research Center Institute of Cytology and GeneticsRussian FederationФедеральный исследовательский центр Институт цитологии и генетики Сибирское отделение Российской академии наук; Институт математики им. С.Л. Соболева, Сибирское отделение Российской академии наукРоссияFederal Research Center Institute of Cytology and Genetics; Sobolev Institute of MathematicsRussian Federation202113072021242156166Copyright © Климонтов В.В., Бериков В.Б., Сайк О.В., 20212021Климонтов В.В., Бериков В.Б., Сайк О.В.Klimontov V.V., Berikov V.B., Saik O.V.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/12665

В обзоре представлены возможности применения искусственного интеллекта для изучения механизмов развития сахарного диабета (СД) и создания новых технологий его профилактики, мониторинга и лечения. В последние годы накоплен огромный массив молекулярных данных, раскрывающих патогенетические механизмы развития СД и его осложнений. Интеллектуальный анализ данных и текстов научных публикаций (data mining и text mining) открывает новые возможности для обработки этой информации. Анализ молекулярно-генетических сетей позволяет выявить молекулярные взаимодействия, важные для развития СД и его осложнений, а ­также идентифицировать новые таргетные молекулы. На основе анализа больших данных и машинного обучения созданы новые платформы для прогноза и скрининга СД, диабетической ретинопатии, хронической болезни почек, сердечно-сосудистых осложнений. Алгоритмы машинного обучения применяются для персонифицированного прогноза уровня глюкозы, создания систем введения инсулина с замкнутым контуром, а также систем поддержки принятия решений по модификации образа жизни и лечению СД. Представляется перспективным применение интеллектуальных систем для анализа больших баз данных, регистров, исследований в реальной клинической практике. Внедрение систем, основанных на искусственном интеллекте, соответствует глобальным трендам современной медицины, в числе которых переход к цифровым и дистанционным технологиям, персонификация лечения, высокоточное прогнозирование и пациентоориентированный подход. Очевидна необходимость дальнейших исследований в этом направлении, с оценкой клинической эффективности новых технологий и их экономическим обоснованием.

This review presents the applications of artificial intelligence for the study of the mechanisms of diabetes development and generation of new technologies of its prevention, monitoring and treatment. In recent years, a huge amount of molecular data has been accumulated, revealing the pathogenic mechanisms of diabetes and its complications. Data mining and text mining open up new possibilities for processing this information. Analysis of gene networks makes it possible to identify molecular interactions that are important for the development of diabetes and its complications, as well as to identify new targeted molecules. Based on the big data analysis and machine learning, new platforms have been created for prediction and screening of diabetes, diabetic retinopathy, chronic kidney disease, and cardiovascular disease. Machine learning algorithms are applied for personalized prediction of glucose trends, in the closed-loop insulin delivery systems and decision support systems for lifestyle modification and diabetes treatment. The use of artificial intelligence for the analysis of large databases, registers, and real-world evidence studies seems to be promising. The introduction of artificial intelligence systems is in line with global trends in modern medicine, including the transition to digital and distant technologies, personification of treatment, high-precision forecasting and patient-centered care. There is an urgent need for further research in this field, with an assessment of the clinical effectiveness and economic feasibility.

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