Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction
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Abstract
Background: Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) algorithms applied to CAC scans may provide significant improvement in prediction of all cardiovascular disease (CVD) events in addition to CHD, including heart failure, atrial fibrillation, stroke, resuscitated cardiac arrest, and all CVD-related deaths. Methods: We applied AI-enabled automated cardiac chambers volumetry and automated calcified plaque characterization to CAC scans (AI-CAC) of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at the baseline examination of the Multi-Ethnic Study of Atherosclerosis (MESA). We used 15-year outcomes data and assessed discrimination using the time-dependent area under the curve (AUC) for AI-CAC versus the Agatston Score. Results: During 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow up for AI-CAC vs Agatston Score was (0.784 vs 0.701), (0.771 vs. 0.709), (0.789 vs.0.712) and (0.816 vs. 0.729) (p<0.0001 for all), respectively. The category-free Net Reclassification Index of AI-CAC vs. Agatston Score at 1-, 5-, 10-, and 15-year follow up was 0.31, 0.24, 0.29 and 0.29 (p<.0001 for all), respectively. AI-CAC plaque characteristics including number, location, and density of plaque plus number of vessels significantly improved NRI for CAC 1-100 cohort vs. Agatston Score (0.342). Conclusion: In this multi-ethnic longitudinal population study, AI-CAC significantly and consistently improved the prediction of all CVD events over 15 years compared with the Agatston score.
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AI-CAC could support evolution toward a more informative, physiologically grounded CAC score, preserving clinical familiarity and, combined with radiomics features or additional image-derived markers such as epicardial adipose tissue, cardiac chamber volumetry, as well as complementary biomarkers such as thoracic aortic calcification and body composition markers if these are shown to be predictive
“AI-CAC could support evolution toward a more informative, physiologically grounded CAC score, preserving clinical familiarity and, combined with radiomics features or additional image-derived markers such as epicardial adipose tissue, cardiac chamber volumetry, as well as complementary biomarkers such as thoracic aortic calcification and body composition markers if these are shown to be predictive”
From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [55]
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Experimental — accuracy not yet measuredAI-CAC enables scalable assessment of vessel-specific calcium burden and plaque density and composition.
“In this context, AI-CAC enables scalable assessment of vessel-specific calcium burden and plaque density and composition.55”
From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [55]
The Methods section (page unavailable) reports plaque location, density, number of plaques, and number of vessels affected. It does not report vessel-specific calcium burden or plaque composition: the study used calcified-plaque characteristics only, and its MESA-1 plaque reports were prepared manually, so scalable assessment of those features is not established. (LLM, conf=0.88)
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Guimarães, A. R. M., Williams, S. E., Macmillan, M. T., Wood, K. L., Wang, C., Weir-McCall, J. R., Adamson, P. D., Roditi, G., Mills, N. L., Dweck, M. R., et al. (2026) · Radiology · doi:10.1148/radiol.251821
Mirjalili, S. R., Atlas, K., Reeves, A. P., Zhang, C., Wasserthal, J., Azimi, A., Hashemi, A., MozafaryBazargany, M., Jolfayi, A. G., Atlas, T., et al. (2026) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2026.101732
Self-citationAI-driven advances in vascular aging research: From mechanisms to precision medicine
Dai, Z.-L., Ma, C.-G., Chen, Y., Fu, L. Y., Jiang, H.-J., & Tai, S. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103247
MOLECULAR AND REGENERATIVE APPROACHES TO THE TREATMENT OF ISCHEMIC HEART DISEASE
Savenkov, D. A., Mambetova, D. R., Saidova, M. U., Ersinoeva, P. M., Piiter, D. A., Popova, M. D., Ковтун, Я. А., Kantemirova, E. R., Mishenin, V. A., Bikkuzhina, Z. F., et al. (2026) · Complex Issues of Cardiovascular Diseases · doi:10.17802/2306-1278-2026-15-3-105-123
Integrating cardiovascular screening in lung cancer screening
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Self-citationCoronary artery calcification: Let the calcium shape the strategy
Scicchitano, P., & Barnaba, I. (2026) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2026.08.012
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- Authors
- Naghavi, M., Reeves, A. P., Atlas, K., Zhang, C., Atlas, T., Henschke, C. I., Yankelevitz, D. F., Budoff, M. J., Li, D., Roy, S. K., Nasir, K., Molloi, S., Fayad, Z., McConnell, M. V., Kakadiaris, I., Maron, D. J., Narula, J., Williams, K., Shah, P. K., Levy, D., & Wong, N. D.
- Affiliations
- Cornell University (US)
- Laboratory for Research on Enterprise and Decisions (TN)
- Mount Sinai Hospital (US)
- The Lundquist Institute (US)
- Houston Methodist (US)
- University of California, Irvine (US)
- Stanford Medicine (US)
- The University of Texas Health Science Center at Houston (US)
- Cedars-Sinai Medical Center (US)
- National Institutes of Health (US)
- Corresponding author
- Naghavi, Morteza
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2024-11-05
- Volume
- 7
- Issue
- 1
- Pages
- 309-309
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC11538462
- OpenAlex
- W4404060993
- Cited by
- 45
- References
- 36
- Citations by year
- 2026: 25 · 2025: 18 · 2024: 2
- FWCI
- 11.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- npj Digital Medicine (publishedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
- PubMed Central (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Health and Human Services (UL1-TR-000040, N01-HC-95159, UL1TR001079, N01-HC-95161, N01-HC-95168, UL1TR001420, N01-HC-95160, N01HC95169, N01-HC-95164, N01-HC-95162, N01-HC-95167, HHSN268201500003I, N01-HC-95165, N01-HC-95163, N01HC95166)
- National Institutes of Health (75N92020D00005, n01hc095162-003, 75N92020D00003, UL1TR001420, n01hc095159-004, N01-HC- 95166, n01hc095160-004, 75N92020D00006, N01-HC9-5168, 75N92020D00001 ,, n01hc095167-003, 5r42ar070713-03, n01hc095165-005, N01-HC- 95159, n01hc095161-004, n01hc095166-004, UL1TR001079, HHSN268201500003I, 75N92020D00004, n01hc095168-003, n01hc095163-003, 75N92020D00007, n01hc095169-004, UL1TR000040, 75N92020D00002, n01hc095164-003, UL1-TR-, N01‐HC‐95161, N01- HC-95163, N01- HC-95165, N01‐HC‐95160, N01 HC95164, N01-HC-95167, N01-HC95162, N01‐HC‐95169, 2R42AR070713)
- National Heart, Lung, and Blood Institute (N01 HC-95159, N01-HC-95160, N01-HC095169, 75N92020D00001, N01 HC-95168, N01-HC-095165, 75N92020D00006, N01 HC95167, (N01-HC-95163, N01 HC095163, 75N92020D00007, 2R42AR070713, N01 HC-95161, N01 HC95165, N01HC95164, 75N92020D00002, N01-HC-095159, N01 HC095168, N01-HC-095162, R01HL146666, N01-HC-095161, 75N92020D00005, HHSN268201500003C, HHSN26820150 0003I, 75N92020D00003, N01 HC095167, N01-HC-095160, N01 HC095166, N01 HC095164, N01‐HC‐95166, N01 HC-95162, 75N92020D00004, N01 HC95169)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (R42 AR070713)
- National Center for Advancing Translational Sciences (N01-HC-95161, N01-HC-95168, UL1-TR, N01-HC-95162, N01-HC-95159, N01-HC-95160, HHSN268201500003I, N01-HC-95166, N01-HC-95169, N01-HC-95163, N01-HC-95167, N01-HC-95165, N01-HC-95164)
- 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 › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, coronary artery calcium, Agatston score, cardiovascular event prediction, coronary artery disease, heart failure, atrial fibrillation, stroke, asymptomatic individuals, Multi-Ethnic Study of Atherosclerosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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