Artificial intelligence in coronary CT angiography: transforming the diagnosis and risk stratification of atherosclerosis.
Authors
Affiliations (4)
Affiliations (4)
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, 1124 West Carson Street, Torrance, CA, 90502, USA. [email protected].
- The Lundquist Institute at Harbor-UCLA Medical Center, 1124 West Carson Street, Torrance, CA, 90502, USA. [email protected].
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, 1124 West Carson Street, Torrance, CA, 90502, USA.
- Department of Medicine, Division of Cardiology, University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA, 90095, USA.
Abstract
Coronary CT Angiography (CCTA) is essential for assessing atherosclerosis and coronary artery disease, aiding in early detection, risk prediction, and clinical assessment. However, traditional CCTA interpretation is limited by observer variability, time inefficiency, and inconsistent plaque characterization. AI has emerged as a transformative tool, enhancing diagnostic accuracy, workflow efficiency, and risk prediction for major adverse cardiovascular events (MACE). Studies show that AI improves stenosis detection by 27%, inter-reader agreement by 30%, and reduces reporting times by 40%, thereby addressing key limitations of manual interpretation. Integrating AI with multimodal imaging (e.g., FFR-CT, PET-CT) further enhances ischemia detection by 28% and lesion classification by 35%, providing a more comprehensive cardiovascular evaluation. This review synthesizes recent advancements in CCTA-AI automation, risk stratification, and precision diagnostics while critically analyzing data quality, generalizability, ethics, and regulation challenges. Future directions, including real-time AI-assisted triage, cloud-based diagnostics, and AI-driven personalized medicine, are explored for their potential to revolutionize clinical workflows and optimize patient outcomes.