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Review Article

Artificial Neural Networks in Cardiovascular Diseases and its Potential for Clinical Application in Molecular Imaging

Author(s): Riccardo Laudicella*, Albert Comelli, Alessandro Stefano, Monika Szostek, Ludovica Crocè, Antonio Vento, Alessandro Spataro, Alessio Danilo Comis, Flavia La Torre, Michele Gaeta, Sergio Baldari and Pierpaolo Alongi

Volume 14, Issue 3, 2021

Published on: 21 June, 2020

Page: [209 - 219] Pages: 11

DOI: 10.2174/1874471013666200621191259

Price: $65

Abstract

In medical imaging, Artificial Intelligence is described as the ability of a system to properly interpret and learn from external data, acquiring knowledge to achieve specific goals and tasks through flexible adaptation. The number of possible applications of Artificial Intelligence is also huge in clinical medicine and cardiovascular diseases. To describe for the first time in literature, the main results of articles about Artificial Intelligence potential for clinical applications in molecular imaging techniques, and to describe its advancements in cardiovascular diseases assessed with nuclear medicine imaging modalities. A comprehensive search strategy was used based on SCOPUS and PubMed databases. From all studies published in English, we selected the most relevant articles that evaluated the technological insights of AI in nuclear cardiology applications. Artificial Intelligence may improve patient care in many different fields, from the semi-automatization of the medical work, through the technical aspect of image preparation, interpretation, the calculation of additional factors based on data obtained during scanning, to the prognostic prediction and risk-- group selection. Myocardial implementation of Artificial Intelligence algorithms in nuclear cardiology can improve and facilitate the diagnostic and predictive process, and global patient care. Building large databases containing clinical and image data is a first but essential step to create and train automated diagnostic/prognostic models able to help the clinicians to make unbiased and faster decisions for precision healthcare.

Keywords: Artificial intelligence, machine-learning, deep Learning, radiomics, SPECT, PET, nuclear Cardiology.

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Graphical Abstract

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