AI applications in noninvasive cardiovascular imaging can quantify prognostic biomarkers and combine imaging parameters with clinical data for individualized risk prediction in coronary artery disease.
Artificial intelligence (AI) describes the use of computational techniques to perform tasks that normally require human cognition. Machine learning and deep learning are subfields of AI that are increasingly being applied to cardiovascular imaging for risk stratification. Deep learning algorithms can accurately quantify prognostic biomarkers from image data. Additionally, conventional or AI-based imaging parameters can be combined with clinical data using machine learning models for individualized risk prediction. The aim of this review is to provide a comprehensive review of state-of-the-art AI applications across various noninvasive imaging modalities (coronary artery calcium scoring CT, coronary CT angiography, and nuclear myocardial perfusion imaging) for the quantification of cardiovascular risk in coronary artery disease. © RSNA, 2021.
Lin et al. (Mon,) studied this question.