Artificial neural network in intravascular imaging

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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

ORCID: 0000-0003-2438-0506

Kolesnikov A.Yu.

Research Institute for Complex Issues of Cardiovascular Diseases

ORCID: 0000-0001-6247-1287

Kochergin N.A.

Research Institute for Complex Issues of Cardiovascular Diseases

ORCID: 0000-0002-1534-264X

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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