Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management

• Published in: Frontiers in cardiovascular medicine Vol. 8; p. 736223
• Main Authors: Maragna, Riccardo, Giacari, Carlo Maria, Guglielmo, Marco, Baggiano, Andrea, Fusini, Laura, Guaricci, Andrea Igoren, Rossi, Alexia, Rabbat, Mark, Pontone, Gianluca
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 22.09.2021
• Subjects: artificial intelligence; Cardiovascular Medicine; coronary artery disease; deep learning; machine learning; multimodality imaging; radiomics
• ISSN: 2297-055X; 2297-055X
• DOI: 10.3389/fcvm.2021.736223

Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.