Abstract
Cardiac computed tomography angiography (CCTA) is widely used as a diagnostic tool for evaluation of coronary artery disease (CAD). Despite the excellent capability to rule-out CAD, CCTA may overestimate the degree of stenosis; furthermore, CCTA analysis can be time consuming, often requiring advanced postprocessing techniques. In consideration of the most recent ESC guidelines on CAD management, which will likely increase CCTA volume over the next years, new tools are necessary to shorten reporting time and improve the accuracy for the detection of ischemia-inducing coronary lesions. The application of artificial intelligence (AI) may provide a helpful tool in CCTA, improving the evaluation and quantification of coronary stenosis, plaque characterization, and assessment of myocardial ischemia. Furthermore, in comparison with existing risk scores, machine-learning algorithms can better predict the outcome utilizing both imaging findings and clinical parameters. Medical AI is moving from the research field to daily clinical practice, and with the increasing number of CCTA examinations, AI will be extensively utilized in cardiac imaging. This review is aimed at illustrating the state of the art in AI-based CCTA applications and future clinical scenarios.
Highlights
Coronary computed tomography angiography (CCTA) represents an excellent tool for the evaluation of patients with suspected stable coronary artery disease (CAD) [1,2,3,4,5,6]
CCTA represents an important step in clinical management of patients with suspected CAD; it is important to keep in mind that the majority of CCTA results in no evidence of significant CAD [12, 13]
The authors focused on the application of an artificial neural network (ANN) with hybrid imaging obtained by the combination of CCTA and myocardial perfusion single photon emission computed tomography (SPECT) [37]
Summary
Coronary computed tomography angiography (CCTA) represents an excellent tool for the evaluation of patients with suspected stable coronary artery disease (CAD) [1,2,3,4,5,6]. CCTA represents an important step in clinical management of patients with suspected CAD; it is important to keep in mind that the majority of CCTA results in no evidence of significant CAD [12, 13]. The application of AI can be helpful in reducing the time of image analysis and rule out patients without evidence of significant disease that may benefit from medical therapy [16]. It can be helpful for detection of myocardial ischemia [17]. In terms of prognostic stratification, AI may play a promising role, identifying algorithms that can stratify the risk of major adverse cardiovascular events (MACE) with high accuracy [18]
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