ISSN / eISSN: 0033-8362 / 1826-6983
editor@radiologiamedicajournal.com
ISSN / eISSN: 0033-8362 / 1826-6983
ISSN / eISSN: 0033-8362 / 1826-6983
editor@radiologiamedicajournal.com
ISSN / eISSN: 0033-8362 / 1826-6983
Dr. Juan Carlos Ramirez¹, Dr. Emily Zhang², Dr. Hassan Al-Mansoori³
1 – Department of Cardiac Imaging, National University of Colombia, Colombia
2 – Department of Radiology, University of California, San Francisco, USA
3 – Cardiology Imaging Division, Qatar University, Qatar
Objective: To evaluate AI-assisted high-resolution CT in quantifying coronary plaque composition and predicting vulnerability to acute coronary events.
Methods: Ninety patients undergoing coronary CT angiography were analyzed. AI software quantified plaque density, volume, and fibrous cap thickness. Findings were correlated with intravascular ultrasound (IVUS) and clinical outcomes over 12 months.
Results: AI-derived measurements correlated strongly with IVUS (r = 0.87). Vulnerable plaques identified by AI had a 3.6-fold higher risk of subsequent cardiac events (p < 0.01).
Conclusion: High-resolution CT combined with AI quantification allows noninvasive, accurate assessment of coronary plaque vulnerability, aiding risk stratification in cardiovascular patients.
Keywords: Coronary CT angiography, plaque vulnerability, artificial intelligence, cardiovascular imaging
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