Cardiovascular Research

Agatston-2.0: A next-generation AI-based coronary calcium quantification approach to improve risk stratification among individua

Authors: Morteza Naghavi|||Amir Azimi|||Kyle Atlas|||Anthony P Reeves|||Chenyu Zhang|||Seyed Reza Mirjalili|||Mohammadhossein Mozafarybazargany|||Amir Ghaffari Jolfayi|||Ali Hashemi|||Thomas Atlas|||Jakob Wasserthal|||Rozemarijn Vliegenthart|||Claudia I Henschke|||David F Yankelevitz|||Andrea D Branch|||Sion K Roy|||Jamal S Rana|||Zahi A Fayad|||Oren Mechanic|||Koen Nieman|||Jagat Narula|||Roxana Mehran|||Prediman K Shah|||John A Rumberger|||Kim A Williams|||Paolo Raggi|||David J Maron|||Michael V McConnell|||Robert A Kloner|||Matthew J Budoff|||Nathan D Wong|||Arthur S Agatston

Affiliations: + Expand

Abstract

A coronary artery calcium (CAC) score of zero using the conventional Agatston scoring method (Agatston-1.0) is associated with very low cardiovascular risk (the "Power of Zero"); however, a small proportion of individuals with CAC=0 still develop coronary heart disease (CHD). Agatston-1.0 relies on thick slices (2.5-3 mm) and a fixed attenuation threshold (≥130 HU), which may miss early, small, low-density, or partially calcified coronary plaques. Agatston-2.0 is an artificial intelligence (AI) framework for automated coronary segmentation and continuous voxel-wise calcium quantification without fixed thresholds, applicable to CT scans with slice thickness ≥0.2 mm and generating an AI-derived CAC score (AI-CAC).

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Reference List

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