Agatston-2.0: A next-generation AI-based coronary calcium quantification approach to improve risk stratification among individuals with zero Agatston scores – Part I

Background 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). Objectives To evaluate the prognostic value of Agatston-2.0 for risk stratification among individuals with a baseline CAC=0. Methods We pooled 3,965 participants with CAC=0 from the Multi-Ethnic Study of Atherosclerosis (MESA, n=2,816) and the Framingham Heart Study (FHS, n=1,149). Associations with incident CHD were evaluated using Cox proportional hazards models with up to 20 years of follow-up. Results An AI-CAC score>0 was detected in 862 participants (21.7%) in population with CAC=0. Participants with AI-CAC>0 had higher 20-year CHD incidence than those with AI-CAC=0 (7.7% vs. 3.8%, p<0.0001). After adjustment for traditional risk factors, AI-CAC>0 remained independently associated with incident CHD (HR 1.71, 95% CI 1.18–2.47). AI-CAC also predicted progression to positive CAC score (adjusted HR 1.95, 95% CI 1.70–2.24). Conclusions The Agatston-2.0 framework identifies clinically meaningful coronary calcification in individuals classified as CAC=0. If validated in additional cohorts, Agatston-2.0 could become the new standard for coronary calcium scoring.