Key result
AI applied to multimodality imaging shows high accuracy for CAD diagnosis and risk stratification.
Why the study?
Increasing demands for imaging in CAD require more efficient, reliable, and sustainable multimodality imaging, prompting an overview of current and future AI applications in ischemic heart disease.
Artificial intelligence and machine learning algorithms show significant promise in automating and enhancing multimodality cardiovascular imaging for the diagnosis and risk stratification of coronary artery disease.
May support AI use in CAD imaging; leaves open need for prospective validation before practice change.
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.
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Maragna et al. (2021) conducted a review in Coronary Artery Disease. Artificial Intelligence vs. Manual assessment or standard clinical evaluation was evaluated. Artificial intelligence algorithms applied to multimodality imaging demonstrate high accuracy and potential to improve the diagnosis and risk stratification of patients with coronary artery disease.
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