cardiotomskСибирский журнал клинической и экспериментальной медициныSiberian Journal of Clinical and Experimental Medicine2713-29272713-265XTSU publishing10.29001/2073-8552-2024-39-2-36-45cardiotomsk-2328Research ArticleОБЗОРЫ И ЛЕКЦИИREVIEWS AND LECTURESМетоды искусственного интеллекта в сердечно-сосудистой хирургии и диагностика патологии аорты и аортального клапана (обзор литературы)Artificial intelligence methods in cardiovascular surgery and diagnosis of pathology of the aorta and aortic valve (literature review)https://orcid.org/0000-0002-9344-5724КимГ. И.KimG. I.

Ким Глеб Ирламович, канд. мед. наук, сердечно-сосудистый хирург, кардиохирургическое отделение

199034, Санкт-Петербург, Университетская наб., 79

190103 Санкт-Петербург, наб. Фонтанки, 154

Gleb I. Kim, Cand. Sci. (Med.), Cardiovascular Surgeon, Cardiac Surgery Department

7-9, Universitetskaya emb., St. Petersburg, 199034

154, the Fontanka River emb., St. Petersburg, 190103

gikim.cor@gmail.comhttps://orcid.org/0000-0002-7305-1429БлекановИ. С.BlekanovI. S.

Блеканов Иван Станиславович, канд. тех. наук, доцент, заведующий кафедрой технологии программирования, факультет прикладной математики – процессов управления

199034, Санкт-Петербург, Университетская наб., 79

Ivan S. Blekanov, Cand. Sci. (Techn.), Associate Professor, Head of the Department of Programming Technology, Faculty of Applied Mathematics and Control Processes

7-9, Universitetskaya emb., St. Petersburg, 199034

i.blekanov@spbu.ruhttps://orcid.org/0009-0007-1468-0042ЕжовФ. В.EzhovF. V.

Ежов Федор Валерьевич, аспирант, факультет прикладной математики – процессов управления

199034, Санкт-Петербург, Университетская наб., 79

Fedor V. Ezhov, Graduate Student, Faculty of Applied Mathematics and Control Processes

7-9, Universitetskaya emb., St. Petersburg, 199034

moremenes@yandex.ruhttps://orcid.org/0009-0007-8233-4387КоваленкоЛ. А.KovalenkoL. A.

Коваленко Лев Алексеевич, аспирант, факультет прикладной математики – процессов управления

199034, Санкт-Петербург, Университетская наб., 79

Lev A. Kovalenko, Graduate Student, Faculty of Applied Mathematics and Control Processes

7-9, Universitetskaya emb., St. Petersburg, 199034

i.blekanov@spbu.ruhttps://orcid.org/0009-0007-6199-3607ЛаринЕ. С.LarinE. S.

Ларин Евгений Сергеевич, аспирант, факультет прикладной математики – процессов управления

199034, Санкт-Петербург, Университетская наб., 79

Evgeniy S. Larin, Graduate Student, Faculty of Applied Mathematics and Control Processes

7-9, Universitetskaya emb., St. Petersburg, 199034

st054551@student.spbu.ruhttps://orcid.org/0000-0003-0189-5013РазумиловЕ. С.RazumilovE. S.

Разумилов Егор Сергеевич, аспирант, факультет прикладной математики – процессов управления

199034, Санкт-Петербург, Университетская наб., 79

Egor S. Razumilov, Graduate Student, Faculty of Applied Mathematics and Control Processes

7-9, Universitetskaya emb., St. Petersburg, 199034

i.blekanov@spbu.ruhttps://orcid.org/0009-0003-9398-3850ПугинК. В.PuginK. V.

Пугин Кирилл Витальевич, аспирант, факультет прикладной математики – процессов управления

199034, Санкт-Петербург, Университетская наб., 79

Kirill V. Pugin, Graduate Student, Faculty of Applied Mathematics and Control Processes

7-9, Universitetskaya emb., St. Petersburg, 199034

st069636@student.spbu.ruhttps://orcid.org/0009-0006-5714-3805ДадашовМ. С.DadashovM. S.

Дадашов Мурад Сахиб оглы, студент, медицинский факультет

199034, Санкт-Петербург, Университетская наб., 79

Murad S. Dadashov, Student, Faculty of Medicine

7-9, Universitetskaya emb., St. Petersburg, 199034

muraddadashov1309@gmail.comhttps://orcid.org/0009-0001-8010-6184ПягайВ. А.PyagayV. A.

Пягай Виктор Александрович, сердечно-сосудистый хирург, кардиохирургическое отделение

199034, Санкт-Петербург, Университетская наб., 79

190103 Санкт-Петербург, наб. Фонтанки, 154

Viktor A. Pyagay, Cardiovascular Surgeon, Cardiac Surgery Department

7-9, Universitetskaya emb., St. Petersburg, 199034

154, the Fontanka River emb., St. Petersburg, 190103

viktorpyagay@mail.ruhttps://orcid.org/0000-0002-1296-8161ШматовД. В.ShmatovD. V.

Шматов Дмитрий Викторович, д-р мед. наук, заместитель директора по медицинской части (кардиохирургия)

199034, Санкт-Петербург, Университетская наб., 79

190103 Санкт-Петербург, наб. Фонтанки, 154

Dmitry V. Shmatov, Dr. Sci. (Med.), Deputy Director for Medical Affairs (Cardiac Surgery)

7-9, Universitetskaya emb., St. Petersburg, 199034

154, the Fontanka River emb., St. Petersburg, 190103

dv.shmatov@gmail.comСанкт-Петербургский государственный университет (СПбГУ); Клиника высоких медицинских технологий имени Н.И. Пирогова, Санкт-Петербургский государственный университет (СПбГУ)РоссияSt. Petersburg State University; St. Petersburg State University Hospital, St. Petersburg State UniversityRussian FederationСанкт-Петербургский государственный университет (СПбГУ)РоссияSt. Petersburg State UniversityRussian Federation2024110720243923645Copyright © Ким Г.И., Блеканов И.С., Ежов Ф.В., Коваленко Л.А., Ларин Е.С., Разумилов Е.С., Пугин К.В., Дадашов М.С., Пягай В.А., Шматов Д.В., 20242024Ким Г.И., Блеканов И.С., Ежов Ф.В., Коваленко Л.А., Ларин Е.С., Разумилов Е.С., Пугин К.В., Дадашов М.С., Пягай В.А., Шматов Д.В.Kim G.I., Blekanov I.S., Ezhov F.V., Kovalenko L.A., Larin E.S., Razumilov E.S., Pugin K.V., Dadashov M.S., Pyagay V.A., Shmatov D.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.sibjcem.ru/jour/article/view/2328

Ведение пациентов с патологией аорты и аортального клапана является крайне актуальной темой. Основная проблема данной патологии – отсутствие явных симптомов до наступления жизнеугрожающего состояния, расслоения или разрыва аорты. Наиболее актуальной в этой ситуации становится ранняя своевременная диагностика, и ведущую роль в этом плане играют визуализирующие методы исследований. Однако основным лимитирующим фактором является скорость и качество оценки изображений. В связи с этим актуальной задачей представляется разработка ассистента врача на основе искусственного интеллекта (ИИ) для интеллектуального анализа изображений (Computer vision, CV). В данной статье сделан обзор современных нейросетевых методов эффективного анализа диагностических изображений (мультиспиральная компьютерная томография (МСКТ) и магнитно-резонансная томография (МРТ)), актуальных для исследования заболеваний сердечно-сосудистой системы в целом и аорты в частности. Одним из главных акцентов данного разбора является исследование применимости современных нейросетевых методов на основе архитектуры Transformer или механизма внимания, демонстрирующих высокие показатели точности в решении широкого спектра задач в других предметных областях и имеющих высокий потенциал применимости для качественного анализа диагностических изображений. Приведен обзор двух фундаментальных задач интеллектуального анализа изображений: классификации (архитектура ResNet, архитектора ViT, архитектора Swin Transformer) и семантической сегментации (2D подходы – U-Net, TransUNet, Swin-Unet, Segmenter и 3D подходы – 3D-Unet, Swin UNETR, VT-UNET). Описанные методы при должной точной настройке и правильном подходе к их обучению позволят эффективно автоматизировать процесс диагностики патологии аорты и аортального клапана. Для успешной реализации проектов в области разработки ИИ следует учитывать ряд ограничений: качественный набор данных, серверные графические станции с мощными видеокартами, наличие междисциплинарной экспертной группы, подготовленные сценарии для тестирования в условиях, приближенных к реальным.

The management of patients with aortic and aortic valve pathology is an extremely relevant task. The main problem of this pathology is the absence of obvious symptoms before the onset of a life–threatening condition, dissection or rupture of the aorta. Early timely diagnosis becomes the most relevant in this situation, and imaging research methods play a leading role in this regard. However, the main limiting factor is the speed and quality of image evaluation. Therefore, an actual task is to develop an AI-based physician assistant for image mining (Computer vision, CV). This article provides an overview of modern neural network methods for effective analysis of diagnostic images (MSCT and MRI) relevant for the study of diseases of the cardiovascular system in general and the aorta in particular. One of the main focuses of this analysis is the study of the applicability of modern neural network methods based on the Transformer architecture or the Attention Mechanism, which show high accuracy rates in solving a wide range of tasks in other subject areas, and have a high potential of applicability for qualitative analysis of diagnostic images. An overview of two fundamental problems of image mining is given: classification (ResNet architecture, ViT architect, Swin Transformer architect) and semantic segmentation (2D approaches – U-Net, TransUNet, Swin-Unet, Segmenter and 3D approaches – 3D-Unet, Swin UNETR, VT-UNET). The described methods, with proper fine tuning and the right approach to their training, will effectively automate the process of diagnosing aortic and aortic valve pathology. For the successful implementation of AI development projects, a number of limitations should be taken into account: a high-quality data set, server graphics stations with powerful graphics cards, an interdisciplinary expert group, prepared scenarios for testing in conditions close to real ones.

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