Resumen

GARMENDIA, Cristian; GONZALO, Nieves; BLANCO, Pablo J.  y  GARCIA-GARCIA, Héctor M.. Implications of Artificial Intelligence in Intravascular Imaging Methods. Rev. argent. cardiol. [online]. 2024, vol.92, n.1, pp.42-54.  Epub 28-Feb-2024. ISSN 1850-3748.  http://dx.doi.org/10.7775/rac.es.v92.i1.20728.

Percutaneous coronary intervention (PCI) is one of the primary revascularization strategies in patients with coronary artery disease (CAD). Several studies support the use of intravascular imaging methods to optimize PCI. However, these methods are underutilized in contemporary clinical practice and face challenges in data interpretation. Therefore, the incorporation of artificial intelligence (AI) is seen as an attractive solution to promote and simplify their use.

AI can be defined as a computer program that mimics the human brain in its ability to collect and process data. Machine learning is a sub-discipline of AI that involves the creation of algorithms capable of analyzing large datasets without making prior assumptions, while deep learning focuses on the construction and training of deep and complex artificial neural networks. The incorporation of AI systems to intravascular imaging methods improves the accuracy of PCI, reduces procedure duration, and minimizes interobserver variability in data interpretation. This promotes their wider adoption and facilitates their use. The aim of this review is to highlight how current AI-based systems can play a key role in the interpretation of data generated by intravascular imaging methods and optimize PCI in patients with CAD.

Palabras clave : Artificial Intelligence; Percutaneous Coronary Intervention; Intravascular Imaging.

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