Imaging of atherosclerotic plaque using coronary CTA (computed tomography angiography) has shown value in estimating patient risk and guiding care decisions. During the 2021 AI and Machine Learning in Cardiovascular CT Series hosted by the society for Cardiovascular Computed Tomography, the medical experts noted that the industry is lacking in standardization of plaque quantification and the way findings are described.
This is an opportunity where AI and other advanced informatics could be applied to coronary CTA images to unlock valuable information on atherosclerotic plaque characteristics that could be used to personalize care decisions.
Atherosclerosis, also known as atherosclerotic cardiovascular disease, takes place when plaque builds up on the coronary artery walls. Atherosclerotic plaque can be made up of fatty substances, cholesterol, cellular waste, calcium, and fibrin. As plaque accumulates on the walls of the coronary arteries, less blood can flow through the arteries due to the narrowing of the vessels. This can result in serious conditions, including heart attack, stroke, and even death.
Deep learning-based atherosclerotic plaque analysis
Even though a lot of people will agree that evaluating coronary atherosclerosis on CTA images could inform patient risk and treatment decisions, it is rarely carried out for the reason that the manual process is time-consuming for clinicians. Deep learning algorithms can be applied to coronary CTA images to automatically classify atherosclerotic plaque and deliver plaque analysis in a standardized manner in order to improve reporting quality and consistency.
Proof suggests that imaging atherosclerotic plaque is essential for estimating patient risk and guiding preventative care decisions. Coronary CTA, which is recommended as a first-line diagnostic test when evaluating patients with stable chest pain, can provide valuable clinical information on coronary atherosclerosis and high-risk plaque features in addition to coronary stenosis.
When imaging atherosclerotic plaque on coronary CTA, it is necessary to make out non-obstructive plaque in the coronary arteries. Researchers have been investigating the way AI techniques can be applied to coronary CTA images to characterize and quantify atherosclerotic plaque for several years. While research reveals that AI can help facilitate the automatic evaluation and characterization of plaque characteristics, several challenges for plaque quantitation remain!
