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

Background Coronary artery calcium (CAC) scoring using the Agatston method is widely applied to detect subclinical coronary artery disease. A CAC score of zero confers low cardiovascular risk, known as the “Power of Zero.” However, a subset of individuals with zero Agatston scores still experience coronary heart disease (CHD) events, underscoring limitations of conventional threshold-based scoring. Objectives To develop and validate an artificial intelligence–based CAC quantification framework (Agatston 2.0) to improve detection of subclinical disease and long-term risk stratification among individuals with baseline Agatston CAC=0. Methods We pooled 3,965 participants with baseline Agatston CAC=0 from the Multi-Ethnic Study of Atherosclerosis (MESA, n=2,816) and the Framingham Heart Study (FHS, n=1,149). Median follow-up in the pooled cohort was 16.2 years, with maximum follow-up of 19.8 years. Agatston 2.0 applied automated deep learning–based coronary artery segmentation and continuous probabilistic calcium quantification without fixed attenuation or size thresholds to generate a continuous AI-derived CAC score (AI-CAC). Outcomes included myocardial infarction (MI), hard CHD (MI, resuscitated cardiac arrest, CHD death), all CHD (hard CHD plus angina with revascularization), and Agatston score progression, defined as conversion to CAC >0 on follow-up CT imaging during MESA Exams 2–5. Multivariable Cox models adjusted for demographics, cardiovascular risk factors, medication use, and cohort. Results Agatston 2.0 detected coronary calcification in 862 participants (21.7%) initially classified as CAC=0 by conventional scoring. At 20 years, cumulative incidence of MI was 4.5% in AI-CAC>0 versus 2.4% in AI-CAC=0 (p=0.0017), hard CHD was 6.4% versus 3.2% (p<0.0001), and all CHD was 7.7% versus 3.8% (p<0.0001). In fully adjusted models, AI-CAC>0 independently predicted MI (HR 1.73, 95% CI 1.06–2.82), hard CHD (HR 1.85, 95% CI 1.23–2.78), and all CHD (HR 1.71, 95% CI 1.18–2.47). AI-CAC>0 also strongly predicted Agatston progression (66.3% vs 43.4% at 10 years; fully adjusted HR 1.95, 95% CI 1.70–2.24; p<0.0001). Among AI-CAC>0 participants, the highest sex-specific tertile demonstrated the greatest risk for all CHD (fully adjusted HR 1.85, 95% CI 1.12–3.07), hard CHD (fully adjusted HR 1.88, 95% CI 1.06–3.32), and Agatston progression (fully adjusted HR 1.87, 95% CI 1.52–2.29). Each 1-SD increase in AI-CAC was associated with an 8–13% higher risk across outcomes. Scan–rescan reproducibility was excellent (r=0.989). Conclusions Agatston 2.0 identifies clinically meaningful subclinical coronary calcification in approximately one in five individuals with conventional CAC=0. The AI-derived score provides independent and durable long-term risk stratification and predicts future calcium progression, refining the biological interpretation of the “Power of Zero.”Method for Agatson 2.0KM analysis