The emergence of a noninvasive imaging modality such as CCTA, now permits quantification not only of plaque burden but also allows for further distinction of plaque components and identification of vulnerable plaques. Application of these findings continues to extend the prospect of coronary CTA in evaluation and management of atherosclerotic coronary artery disease (CAD) in clinical practice. In the future artificial intelligence and machine learning will play a significant role in plaque analysis allowing for high accuracy and reproducibility which will lead to a substantial increase in the utilization of coronary CTA.
Keyphrases
- coronary artery disease
- artificial intelligence
- machine learning
- percutaneous coronary intervention
- cardiovascular events
- coronary artery bypass grafting
- big data
- clinical practice
- deep learning
- aortic stenosis
- current status
- high resolution
- coronary artery
- heart failure
- risk factors
- ejection fraction
- data analysis