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Arnt A.A.

Research Institute for Complex Issues of Cardiovascular Diseases

Kolesnikov A.Yu.

Research Institute for Complex Issues of Cardiovascular Diseases

Kochergin N.A.

Research Institute for Complex Issues of Cardiovascular Diseases

Artificial neural network in intravascular imaging

Authors:

Arnt A.A., Kolesnikov A.Yu., Kochergin N.A.

More about the authors

Journal: Russian Journal of Cardiology and Cardiovascular Surgery. 2024;17(1): 77‑81

DOI: 10.17116/kardio20241701177

Read: 1537 times

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Table of contents

ARTIFICIAL NEURAL NETWORK IN INTRAVASCULAR IMAGING
ARTIFICIAL NEURAL NETWORK IN INTRAVASCULAR IMAGING

To cite this article:

Arnt AA, Kolesnikov AYu, Kochergin NA. Artificial neural network in intravascular imaging. Russian Journal of Cardiology and Cardiovascular Surgery. 2024;17(1):77‑81. (In Russ.)
https://doi.org/10.17116/kardio20241701177

Подписка 2026 Кардиология и сердечно-сосудистая хирургия (Russian Journal of Cardiology and Cardiovascular Surgery)
МСФ на ЯМ
Использование инфографики авторами научных работ
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Abstract:

Intravascular imaging has a great potential to improve the outcomes in patients with coronary artery disease. Currently, there are several methods of intravascular visualization verifying the components of vascular wall and morphology of atherosclerotic lesion. However, interpretation of images has divergences between various experts that complicates intravascular diagnostics. To date, artificial neural networks in evaluation of diagnostic data are being actively developed. Neural network training in intravascular detection of morphological elements of vascular wall, signs of atherosclerotic plaque vulnerability and hemodynamic significance of lesions will be valuable to eliminate some biases caused by human factor and reduce duration of endovascular intervention. This article presents the experience of artificial neural networks in interpreting the results of intravascular imaging, such as intravascular ultrasound and optical coherence tomography.

Keywords:

artificial neural networks
machine learning
intravascular imaging
optical coherence tomography
intravascular ultrasound

Authors:

Arnt A.A.

Research Institute for Complex Issues of Cardiovascular Diseases

Kolesnikov A.Yu.

Research Institute for Complex Issues of Cardiovascular Diseases

Kochergin N.A.

Research Institute for Complex Issues of Cardiovascular Diseases

Received:

06.02.2023

Accepted:

20.02.2023

Conflict of Interest :

The research was supported by the Russian Science Foundation grant No. 23-75-10009, https://rscf.ru/project/23-75-10009/

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