Agatston-2.0: A next-generation AI-based coronary calcium quantification approach to improve risk stratification among individuals with zero Agatston scores – Part I
Open accessAbstract
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.
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AI methods may detect subthreshold coronary calcification not captured by conventional Agatston scoring, as in the Agatston 2.0 framework, which uses a relative-density approach with internal calibration to extend assessment below the conventional 130-HU threshold, potentially refining risk stratification within the zero CAC category.
“AI methods may also detect subthreshold coronary calcification not captured by conventional Agatston scoring, as in the Agatston 2.0 framework, which uses a relative-density approach with internal calibration to extend assessment below the conventional 130-HU threshold, potentially refining risk stratification within the zero CAC category.58”
From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [58]
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Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review
McDonald, V. J. F., Parsa, S., Mastrodicasa, D., Rodriguez, F., Langlotz, C., Maron, D. J., Sandhu, A. T., Dudum, R., & Jain, S. S. (2026) · BMJ Digital Health & AI · doi:10.1136/bmjdh-2026-000053
Self-citationBeyond the “Power of Zero”: Rethinking coronary calcium scoring in the AI Era
Al-Kindi, S. G., & Villines, T. C. (2026) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2026.101744
- Authors
- Naghavi, M., Azimi, A., Atlas, K., Reeves, A. P., Zhang, C., Mirjalili, S. R., Mozafarybazargany, M., Jolfayi, A. G., Hashemi, A., Atlas, T., Wasserthal, J., Vliegenthart, R., Henschke, C. I., Yankelevitz, D. F., Branch, A. D., Roy, S. K., Rana, J. S., Fayad, Z. A., Mechanic, O., Nieman, K., Narula, J., Mehran, R., Shah, P. K., Rumberger, J. A., Sr, K. A. W., Raggi, P., Maron, D. J., McConnell, M. V., Kloner, R. A., Budoff, M. J., Wong, N. D., & Agatston, A. S.
- Affiliations
- Cornell University (US)
- Laboratory for Research on Enterprise and Decisions (TN)
- University Hospital of Basel (CH)
- University Medical Center Groningen (NL)
- Icahn School of Medicine at Mount Sinai (US)
- The Lundquist Institute (US)
- Kaiser Permanente Oakland Medical Center (US)
- Stanford Medicine (US)
- University of Houston (US)
- Cedars-Sinai Medical Center (US)
- Princeton University (US)
- University of Alberta (CA)
- University of Southern California (US)
- University of California, Irvine (US)
- Corresponding author
- Naghavi, Morteza
- ORCID
- Naghavi, Morteza
- Azimi, Amir
- Atlas, Kyle
- Reeves, Anthony P.
- Zhang, Chenyu
- Mozafarybazargany, Mohammadhossein
- Jolfayi, Amir Ghaffari
- Atlas, Thomas
- Wasserthal, Jakob
- Vliegenthart, Rozemarijn
- Henschke, Claudia I.
- Yankelevitz, David F.
- Branch, Andrea D.
- Rana, Jamal S.
- Fayad, Zahi A.
- Nieman, Koen
- Narula, Jagat
- Mehran, Roxana
- Shah, Prediman K.
- Rumberger, John A.
- Raggi, Paolo
- Maron, David J.
- McConnell, Michael V.
- Kloner, Robert A.
- Budoff, Matthew J.
- Wong, Nathan D.
- Agatston, Arthur S.
- Venue
- American Journal of Preventive Cardiology
- Publisher
- Elsevier BV
- Published
- 2026-12
- Volume
- 30
- Pages
- 101698
- ISSN
- 2666-6677 (ISSN-L)
- PMC
- PMC13632017
- OpenAlex
- W7163637978
- Cited by
- 2
- References
- 43
- Citations by year
- 2026: 2
- FWCI
- 9.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- American Journal of Preventive Cardiology (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- NIH
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Surgery › Coronary Interventions and Diagnostics
- Keywords
- coronary artery calcium, coronary artery calcium score, incident coronary heart disease, cardiovascular risk prediction
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