Title:Artificial Neural Networks in Cardiovascular Diseases and its Potential for Clinical Application in Molecular Imaging
Volume: 14
Issue: 3
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
Affiliation:
- Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98125 Messina,Italy
Keywords:
Artificial intelligence, machine-learning, deep Learning, radiomics, SPECT, PET, nuclear Cardiology.
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.