Agatston-2.0: A next-generation AI-based coronary calcium quantification approach to improve risk stratification among individuals with zero Agatston scores – Part I
Year: 2026
Type: article
Abstract: 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.
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 Sr, Paolo Raggi, David J. Maron, Michael V. McConnell, Robert A. Kloner, Matthew J. Budoff, Nathan D. Wong, Arthur S. Agatston
Institutions: Cornell University, Laboratory for Research on Enterprise and Decisions, University Hospital of Basel, University Medical Center Groningen, Icahn School of Medicine at Mount Sinai, The Lundquist Institute, Kaiser Permanente Oakland Medical Center, Stanford Medicine, Stanford University, University of Houston, Cedars-Sinai Medical Center, Princeton University, University of Louisville, University of Alberta, University of Southern California, Huntington Medical Research Institutes, University of California, Irvine
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,900
Awards: G1261793039, G1276754516, G1294553593, G1539384734, G1640378483, G1803853571, Wake Forest Clinical and Translational Science Award, G1912130661, G1983464145, G2095942735, G2144938473, G22458031, G2580996367, G2726417417, G2837638605, G290884426, G291913881, G3093995152, G3563251057, G3570589137, G3581052147, G3595232572, G3667269547, G3990748671, G4309390892, G4345352953, G4401650913, G4497505643, G4563812999, G4590187187, G4976486911, G4986157935, G5075046117, G5108002584, G514410172, G5653954166, G5654179670, G566787130, Institute for Clinical and Translational Research, G6443373778, G66341962, G6742279884, G6856766826, G7166352866, G78770644, G805458286, Clinical and Translational Science Award, G8145863542, G8638601890, G8761005068, G8929982112, G9000004549, G9000004784, G9000006353, G9000017360, G9000019818, G9000025517, G9000063950, G9000065698, G991334795
OpenAlex ID: W7163637978