Serial analysis of coronary artery disease progression by artificial intelligence assisted coronary computed tomography angiography: early clinical experience.
Geoffrey W ChoLauren AndersonCarlos G QuesadaRobert S JenningsJames K MinJames P EarlsRonald P KarlsbergPublished in: BMC cardiovascular disorders (2022)
We performed the first AI augmented CCTA based serial analysis of changes in coronary plaque characteristics over 13 years. We were able to consistently assess progression of plaque volumes, stenosis, and APCs with this novel methodology. We found a significant increase in TPV composed of decreasing LD-NCP, and increasing NCP and CP, with variations in the evolution of APCs between vessels. Although the significance of evolving APCs needs to be investigated, this case demonstrates AI-based CCTA analysis can serve as valuable clinical tool to accurately define unique CAD characteristics over time. Prospective trails are needed to assess whether quantification of APCs provides prognostic capabilities to improve clinical care.
Keyphrases
- coronary artery disease
- artificial intelligence
- machine learning
- big data
- cardiovascular events
- coronary artery bypass grafting
- percutaneous coronary intervention
- deep learning
- coronary artery
- healthcare
- palliative care
- aortic stenosis
- computed tomography
- magnetic resonance imaging
- atomic force microscopy
- ejection fraction
- health insurance
- single molecule