Artificial Intelligence in Cardiovascular Imaging for Risk Stratification in Coronary Artery Disease.
Andrew LinMarton KolossvaryManish MotwaniIvana IšgumPál Maurovich HorvatPiotr J SlomkaDamini DeyPublished in: Radiology. Cardiothoracic imaging (2021)
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.
Keyphrases
- artificial intelligence
- deep learning
- machine learning
- big data
- coronary artery disease
- high resolution
- coronary artery
- electronic health record
- heart failure
- percutaneous coronary intervention
- type diabetes
- cardiovascular disease
- mild cognitive impairment
- induced pluripotent stem cells
- pulmonary hypertension
- atrial fibrillation
- cardiovascular events
- contrast enhanced
- aortic stenosis