Evidence map›Paper›PMID 42828238›Full record

ArticleAmerican journal of preventive cardiology2026

Agatston-2.0: A next-generation AI-based coronary calcium quantification approach to improve risk stratification among individuals with zero Agatston scores - Part I: An AI-CVD Study within the Multi-Ethnic Study of Atherosclerosis and Framingham Heart Study.

Morteza Naghavi, Amir Azimi, Kyle Atlas, Anthony P Reeves, Chenyu Zhang, Seyed Reza Mirjalili, Mohammadhossein Mozafarybazargany, Amir Ghaffari Jolfayi, Ali Hashemi, Thomas Atlas and 22 more

Abstract read
In one paragraph

Article in American journal of preventive cardiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

32 authors.

Morteza NaghaviHeartLung.AI, Houston, TX 77021, USA.
Amir AzimiHeartLung.AI, Houston, TX 77021, USA.
Kyle AtlasHeartLung.AI, Houston, TX 77021, USA.
Anthony P ReevesDepartment of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA.
Chenyu ZhangHeartLung.AI, Houston, TX 77021, USA.
Seyed Reza MirjaliliHeartLung.AI, Houston, TX 77021, USA.
Mohammadhossein MozafarybazarganyHeartLung.AI, Houston, TX 77021, USA.
Amir Ghaffari JolfayiHeartLung.AI, Houston, TX 77021, USA.
Ali HashemiHeartLung.AI, Houston, TX 77021, USA.
Thomas AtlasTustin Teleradiology, Tustin, CA 92780, USA.
Jakob WasserthalClinic of Radiology and Nuclear Medicine, University Hospital Basel, Basel, Switzerland.
Rozemarijn VliegenthartDepartment of Radiology, University Medical Center Groningen, Groningen, the Netherlands.
Claudia I HenschkeDepartment of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
David F YankelevitzDepartment of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Andrea D BranchDepartment of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Sion K RoyThe Lundquist Institute, Torrance, CA 90502, USA.
Jamal S RanaDivision of Cardiology, Kaiser Permanente Oakland Medical Center, Oakland, CA, USA.
Zahi A FayadBioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Oren MechanicThe Agatston Center, Miami Beach, FL, USA.
Koen NiemanDepartments of Medicine and Radiology, Stanford University, Stanford, CA, USA.
Jagat NarulaUniversity of Houston, Houston, TX 77030, USA.
Roxana MehranDepartment of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Prediman K ShahCedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
John A RumbergerDepartment of Cardiac Imaging, Princeton Longevity Center, Princeton, NJ, USA.
Kim A Williams SrUniversity of Louisville, Louisville, KY 40202, USA.
Paolo RaggiDivision of Cardiology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
David J MaronStanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA.
Michael V McConnellStanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA.
Robert A KlonerHuntington Medical Research Institutes, Pasadena, CA 91105, USA.
Matthew J BudoffCedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
Nathan D WongHeart Disease Prevention Program, Mary and Steve Wen Cardiovascular Division, University of California Irvine, Irvine, CA 92697, USA.
Arthur S AgatstonThe Agatston Center, Miami Beach, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Agatston scoreArtificial intelligenceCoronary artery calciumCoronary heart disease

Identifiers

PMID42828238
PMCPMC13632017

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.