ARTIFICIAL INTELLIGENCE TECHNOLOGY IN ASSESSING MYOCARDIAL PERFUSION USING POSITRON EMISSION TOMOGRAPHY USING 82Rb-CHLORIDE

Abstract

At the moment, one of the most common causes of morbidity and mortality is coronary heart disease, which determines the need to develop methods for its diagnosis. Among diagnostic methods, non-invasive methods occupy a special place, in particular, determination of myocardial perfusion. One of the “gold standards” for assessing cardiac muscle perfusion is positron emission tomography combined with computed tomography (PET/CT) with 82Rb-chloride. Recently, attempts have been actively made to introduce the use of artificial intelligence in a variety of areas of medical clinical practice, including the development of medical decision support systems, as well as neural networks for assessing the results of diagnostic studies. In particular, there is information about attempts to use artificial intelligence in assessing myocardial perfusion using PET/CT with 82Rb-chloride. This paper analyzes the possibilities and prospects for using artificial intelligence in assessing the results of PET/CT with 82Rb-chloride. The use of well-trained neural networks and machine learning algorithms can significantly increase the accuracy of diagnosing coronary heart disease by improving the quality of images, analyzing the data obtained, or calculating characteristics and indicators, the quantitative interpretation of which may be difficult for a doctor. Neural networks are able to take into account in the prognosis both clinical and anamnestic data and additional parameters determined from research data, which the doctor may not pay attention to, which determines the relevance and prospects for the use of artificial intelligence in relation to the interpretation of 82Rb-PET/CT results.