Artificial intelligence methods in cardiovascular surgery and diagnosis of pathology of the aorta and aortic valve (literature review)-Reference-Cited by-同舟云学术

Artificial intelligence methods in cardiovascular surgery and diagnosis of pathology of the aorta and aortic valve (literature review)

Published:2024-07-11 Issue:2 Volume:39 Page:36-45
ISSN:2713-265X
Container-title:Siberian Journal of Clinical and Experimental Medicine
language:
Short-container-title:SJCEM

Author:

Kim G. I.¹^ORCID,Blekanov I. S.²^ORCID,Ezhov F. V.²^ORCID,Kovalenko L. A.²^ORCID,Larin E. S.²^ORCID,Razumilov E. S.²^ORCID,Pugin K. V.²^ORCID,Dadashov M. S.²^ORCID,Pyagay V. A.¹^ORCID,Shmatov D. V.¹^ORCID

Affiliation:

1. St. Petersburg State University; St. Petersburg State University Hospital, St. Petersburg State University

2. St. Petersburg State University

Abstract

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.

Publisher

Cardiology Research Institute

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