Myocardial perfusion imaging with single photon emission computed tomography or positron emission tomography is commonly used for diagnosis and risk stratification in patients with known or suspected coronary artery disease. Current scanners often incorporate computed tomography for attenuation correction, resulting in a wealth of clinical and imaging information associated with a typical study. Novel highly efficient artificial intelligence (AI) tools have emerged, revolutionizing image analysis with direct and accurate extraction of information from cardiovascular images. These methods have accuracy similar or better to expert interpretation, without the need for timely manual adjustments or measurements. Additionally, artificial intelligence-based algorithms have been developed to integrate the large volume of clinical and imaging information to improve disease diagnosis and risk estimation. Lastly, explainable AI techniques are being developed, overcoming the traditional perception of AI as a "black box" by presenting the rationale for the computed decision or recommendation through attention maps and individualized explanations of risk estimates. In this review we focus on these applications of the latest AI tools in nuclear cardiology and non-contrast cardiac CT.
Copyright © 2020. Published by Elsevier Inc.