vtioВестник трансплантологии и искусственных органовRussian Journal of Transplantology and Artificial Organs1995-1191Academician V.I.Shumakov National Medical Research Center of Transplantology and Artificial Organs", Ministry of Health of the Russian Federation10.15825/1995-1191-2021-2-177-182vtio-1378Research ArticleОБЗОРЫ ЛИТЕРАТУРЫLITERATURE REVIEWSПерспективы применения искусственных нейронных сетей для решения задач клинической трансплантологииProspects for the use of artificial neural networks for problem solving in clinical transplantationКурабековаР. М.KurabekovaR. M.

Москва

Moscow

БельченковА. А.BelchenkovA. A.

Москва

Moscow

ШевченкоО. П.ShevchenkoO. P.

Шевченко Ольга Павловна

123182, Москва, ул. Щукинская, д. 1

Olga P. Shevchenko

1, Shchukinskaya str., Moscow, 123182

ФГБУ «Национальный медицинский исследовательский центр трансплантологии и искусственных органов имени академика В.И. Шумакова» Минздрава РоссииРоссияShumakov National Medical Research Center of Transplantology and Artificial OrgansRussian FederationФГБУ «Национальный медицинский исследовательский центр трансплантологии и искусственных органов имени академика В.И. Шумакова» Минздрава России; ФГАОУ ВО Первый Московский государственный медицинский университет имени И.М. Сеченова Минздрава России (Сеченовский университет)РоссияShumakov National Medical Research Center of Transplantology and Artificial Organs; Sechenov UniversityRussian Federation202112072021232177182Copyright © Курабекова Р.М., Бельченков А.А., Шевченко О.П., 20212021Курабекова Р.М., Бельченков А.А., Шевченко О.П.Kurabekova R.M., Belchenkov A.A., Shevchenko O.P.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://journal.transpl.ru/vtio/article/view/1378

Ведение реципиентов солидных органов требует значительного объема исследований и наблюдений на протяжении всей жизни реципиента, что сопряжено с накоплением больших массивов информации, требующей структурирования и последующего анализа. Такие информационные технологии, как машинное обучение, нейронные сети и другие инструменты искусственного интеллекта, позволяют анализировать так называемые «большие данные». Технологии машинного обучения основаны на концепции машины, имитирующей человеческий интеллект, и позволяют выявлять закономерности, недоступные традиционным методам. Примеры применения программ искусственного интеллекта в трансплантологии пока немногочисленны, но в последние годы их число заметно увеличивается. Представлен обзор данных современной литературы по применению систем искусственного интеллекта в трансплантологии.

Management of solid organ recipients requires a significant amount of research and observation throughout the recipient’s life. This is associated with accumulation of large amounts of information that requires structuring and subsequent analysis. Information technologies such as machine learning, neural networks and other artificial intelligence tools make it possible to analyze the so-called ‘big data’. Machine learning technologies are based on the concept of a machine that mimics human intelligence and and makes it possible to identify patterns that are inaccessible to traditional methods. There are still few examples of the use of artificial intelligence programs in transplantology. However, their number has increased markedly in recent years. A review of modern literature on the use of artificial intelligence systems in transplantology is presented.

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