AI Article Synopsis
- - coronary artery disease is a major global cause of death, and its impact is linked to both the narrowing of coronary arteries and the presence of atherosclerotic plaque components
- - coronary computed tomography angiography (CCTA) allows non-invasive monitoring of plaque, but accurately assessing plaque features remains a challenge for doctors
- - artificial intelligence (AI) has made significant advancements in cardiovascular imaging, enhancing CCTA by improving efficiency, providing objective assessments, and aiding treatment decisions, while the review also addresses ongoing issues and future possibilities for AI in clinical practice
Article Abstract
Coronary artery disease is a leading cause of death worldwide. Major adverse cardiac events are associated not only with coronary luminal stenosis but also with atherosclerotic plaque components. Coronary computed tomography angiography (CCTA) enables non-invasive evaluation of atherosclerotic plaque along the entire coronary tree. However, precise and efficient assessment of plaque features on CCTA is still a challenge for physicians in daily practice. Artificial intelligence (AI) refers to algorithms that can simulate intelligent human behavior to improve clinical work efficiency. Recently, cardiovascular imaging has seen remarkable advancements with the use of AI. AI-assisted CCTA has the potential to facilitate the clinical workflow, offer objective and repeatable quantitative results, accelerate the interpretation of reports, and guide subsequent treatment. Several AI algorithms have been developed to provide a comprehensive assessment of atherosclerotic plaques. This review serves to highlight the cutting-edge applications of AI-assisted CCTA in atherosclerosis plaque characterization, including detecting obstructive plaques, assessing plaque volumes and vulnerability, monitoring plaque progression, and providing risk assessment. Finally, this paper discusses the current problems and future directions for implementing AI in real-world clinical settings.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11262402 | PMC |
| http://dx.doi.org/10.31083/j.rcm2501027 | DOI Listing |
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