Cardiac computed tomography enables comprehensive, noninvasive imaging of the coronary vasculature, and is used to assess luminal stenoses, coronary calcifications, and distinct adverse plaque characteristics, helping to identify patients prone to future events. Novel software tools, implementing artificial intelligence solutions, can automatically quantify and characterize atherosclerotic plaque from standard computed tomography datasets. These quantitative imaging biomarkers have been shown to improve patient risk stratification beyond clinical risk scores and current clinical interpretation of cardiac computed tomography. In addition, noninvasive molecular imaging in higher risk patients can be used to assess plaque activity and plaque thrombosis. Noninvasive imaging allows unique insight into the burden, morphology and activity of atherosclerotic coronary plaques. Such phenotyping of atherosclerosis can potentially improve individual patient risk prediction, and in the near future has the potential for clinical implementation.
Keyphrases
- coronary artery disease
- computed tomography
- artificial intelligence
- high resolution
- end stage renal disease
- ejection fraction
- percutaneous coronary intervention
- coronary artery
- newly diagnosed
- cardiovascular events
- chronic kidney disease
- magnetic resonance imaging
- machine learning
- healthcare
- cardiovascular disease
- aortic stenosis
- deep learning
- prognostic factors
- high throughput
- case report
- primary care
- quality improvement
- current status
- heart failure
- left ventricular
- emergency department
- acute coronary syndrome
- contrast enhanced
- type diabetes
- aortic valve
- climate change