Inteligência artificial na fisiologia coronária: onde estamos?

Abstract

The use of invasive coronary physiology to select individuals for coronary revascularization has been established in current guidelines for the management of stable coronary artery disease. Compared to angiography alone, coronary physiology has been proven to improve clinical outcomes and cost-effectiveness in the revascularization process. Randomized controlled trials, however, have questioned the efficacy of ischemia testing in selecting individuals for revascularization. After an angiographically successful percutaneous coronary intervention, 20 to 40% of patients experienced persistent or recurrent angina. Artificial intelligence is defined as the usage of various algorithms and computational concepts to perform the complex tasks in an efficient manner. It can be classified into two types: unsupervised and supervised approaches. Supervised learning is majorly used for the regression and classification tasks, and in this optimized mapping between output variables and paired input is carried out to perform the tasks. In contrast to this, unsupervised learning works in the different manner. In unsupervised learning, output variables data is not available and further clusters and relations between input data are found out by using the various algorithms. To acquire more abstract representation of data, deep learning technology, which uses the multilayer neural networks, dominates the artificial learning nowadays.