TP Abdualimov¹* and AG Obrezan^1,2

¹Department of General Practice and Therapy, My Medical Center, Saint-Petersburg, Russian Federation
²Department of Hospital Therapy, St. Petersburg State University, Saint-Petersburg, Russian Federation

*Corresponding Author: TP Abdualimov, Department of General Practice and Therapy, My Medical Center, Saint-Petersburg, Russian Federation.

Received: January 27, 2023; Published: February 16, 2023

Abstract

A novel approach to diagnosing coronary artery disease was proposed. A model for diagnosing coronary heart disease was designed using neural network analysis and allow to reveal transient myocardial ischemia, pathology of the main coronary arteries. The aim of the study was to compare the accuracy of the trained neural network model on the input structured data (sex and age, cholesterol levels, presence of chronic diseases, hereditary factors, lifestyle and etc.) and ECG images with the results of traditional coronary angiography. The proposed diagnostic model was proved to be reliable and highly sensitive for 1500150 cases. The model was compared with the traditional diagnostic methods of transient myocardial ischemia (24-hour Holter monitoring, treadmill test), where the presented diagnostic model was considered to be significantly effective. The accuracy of forecasts was assessed and justified by the cardiologists supervising patients with ACS on a daily basis. The study also presents a new method of sample extrapolation using generative adversarial networks allowing to exceed the volume of observations used in classical meta-analyses.

Keywords: Coronary Arteries; Neural Networks; Artificial Intelligence; Coronary Heart Disease; Deep Learning; ECG; Non-invasive Predictive AI Coronary Angiography

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Citation

Citation: TP Abdualimov and AG Obrezan. “Non-invasive Predictive AI Coronary Angiography”.Acta Scientific Medical Sciences 7.3 (2023): 123-132.

Copyright

Copyright: © 2022 TP Abdualimov and AG Obrezan. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.