ssmuБюллетень сибирской медициныBulletin of Siberian Medicine1682-03631819-3684Siberian State Medical University, the Ministry of Healthcare of the Russian Federation10.20538/1682-0363-2021-4-193-204ssmu-4596Research ArticleОБЗОРЫ И ЛЕКЦИИREVIEW AND LECTURESИскусственные нейронные сети в кардиологии: анализ графических данныхArtificial neural networks in cardiology: analysis of graphic datahttps://orcid.org/0000-0003-2404-2873ОнищенкоП. С.OnishchenkoP. S.

мл. науч. сотрудник, 650002, г. Кемерово, Сосновый бульвар, 6;

аспирант, лаборатория новых биоматериалов, 630090, г. Новосибирск, пр. Академика Лаврентьева, 6 

 

6, Sosnovi Blv., Kemerovo, 650002;

 6, Akademika Lavrentieva Av., Novosibirsk, 630090

onis.pavel@gmail.comКлышниковК. Ю.KlyshnikovK. Yu.

науч. сотрудник, лаборатория новых биоматериалов, ИВТ СО РАН, г. Новосибирск. ORCID0000-0003-3211-1250

6, Sosnovi Blv., Kemerovo, 650002

https://orcid.org/0000-0001-7477-3979ОвчаренкоЕ. А.OvcharenkoE. A.

канд. техн. наук, зав. лабораторией новых биоматериалов, 

 

6, Sosnovi Blv., Kemerovo, 650002

Научно-исследовательский институт комплексных проблем сердечно-сосудистых заболеваний (НИИ КПССЗ); Институт вычислительных технологий Сибирского отделения Российской академии наук (ИВТ СО РАН)РоссияResearch Institute for Complex Issues of Cardiovascular Diseases; Science Institute of Computational Technologies of the Siberian Branch of the Russian Academy of SciencesRussian FederationНаучно-исследовательский институт комплексных проблем сердечно-сосудистых заболеваний (НИИ КПССЗ)РоссияResearch Institute for Complex Issues of Cardiovascular DiseasesRussian Federation202104012022204193204Copyright © Онищенко П.С., Клышников К.Ю., Овчаренко Е.А., 20222022Онищенко П.С., Клышников К.Ю., Овчаренко Е.А.Onishchenko P.S., Klyshnikov K.Y., Ovcharenko E.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://bulletin.ssmu.ru/jour/article/view/4596

Рассмотрены области применения сверточных нейронных сетей для обработки медицинских изображений в различных сферах кардиологии и кардиохирургии на примере публикаций с 2016 по 2019 г.

В данной работе использовались следующие базы научных статей: PubMed Central, ArXiv, ResearchGate. Приведенные работы структурировались по области интереса (сердце, аорта, сонные артерии).

Описан общий принцип работы рассматриваемой технологии, показаны результаты и рассмотрены основные области применения данной технологии в анализируемых работах. Для большинства приведенных исследований приведены объемы выборок, авторское видение развития сверточных нейронных сетей в медицине и перечислены некоторые ограничивающие факторы для их распространения.

Показаны возможные сферы применения сверточных нейронных сетей в области кардиологии и кардиохирургии. Не отрицая существующие проблемы, такой тип искусственных нейронных сетей в будущем может стать верным помощником для широкого спектра врачей и исследователей. 

Aim

Aim. To consider application of convolutional neural networks for processing medical images in various fields of cardiology and cardiac surgery using publications from 2016 to 2019 as an example.

Materials and methods

Materials and methods. In the study, we used the following scientific databases: PubMed Central, ArXiv, ResearchGate. The cited publications were grouped by the area of interest (heart, aorta, carotid arteries).

Results

Results. The general principle of work of the technology under consideration was described, the results were shown, and the main areas of application of this technology in the studies under consideration were described. For most of the studies, sample sizes were given. The author’s view on the development of convolutional neural networks in medicine was presented and some limiting factors for their distribution were listed.

Conclusion

Conclusion. A brief overview shows possible areas of application of convolutional neural networks in the fields of cardiology and cardiac surgery. Without denying the existing problems, this type of artificial neural networks may help many doctors and researchers in the future. 

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