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Cited by 16Cardiac Imaging and DiagnosticsArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes

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Gennari, Antonio G., Rossi, Alexia, De Cecco, Carlo N., van Assen, Marly, Sartoretti, Thomas, Giannopoulos, Andreas A., Schwyzer, Moritz, Huellner, Martin W. +1 more·2024·The International Journal of Cardiovascular Imaging
DOI 10.1007/s10554-024-03080-4PMC PMC11147943
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Abstract

Almost 35 years after its introduction, coronary artery calcium score (CACS) not only survived technological advances but became one of the cornerstones of contemporary cardiovascular imaging. Its simplicity and quantitative nature established it as one of the most robust approaches for atherosclerotic cardiovascular disease risk stratification in primary prevention and a powerful tool to guide therapeutic choices. Groundbreaking advances in computational models and computer power translated into a surge of artificial intelligence (AI)-based approaches directly or indirectly linked to CACS analysis. This review aims to provide essential knowledge on the AI-based techniques currently applied to CACS, setting the stage for a holistic analysis of the use of these techniques in coronary artery calcium imaging. While the focus of the review will be detailing the evidence, strengths, and limitations of end-to-end CACS algorithms in electrocardiography-gated and non-gated scans, the current role of deep-learning image reconstructions, segmentation techniques, and combined applications such as simultaneous coronary artery calcium and pulmonary nodule segmentation, will also be discussed.

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  • More than 60 published and proprietary AI algorithms for CAC detection and scoring on chest imaging have been reviewed in the last 2 years, varying in populations, imaging protocols, training datasets, validation approaches and model architectures.

    “More than 60 published and proprietary AI algorithms for CAC detection and scoring on chest imaging (AI-CAC) have been reviewed in the last 2 years,12–15 varying in populations, imaging protocols, training datasets, validation approaches and model architectures.”

    From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [13]

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Reviews of AI-CAC generally focused on algorithmic performance rather than clinical implementation.

    “Reviews generally focused on algorithmic performance rather than clinical implementation.”

    From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [13]

    The Abstract says this review focuses on the evidence, strengths, and limitations of AI-CAC algorithms, and the CACS quantification section reports numerous performance measures. The Conclusions also address clinical implementation. The paper therefore provides an example of a review with substantial attention to algorithmic performance, but it does not establish that reviews generally prioritize it over clinical implementation. (LLM, conf=0.79)

    Experimental — accuracy not yet measured

  • AI algorithms can automate CAC detection and accurately quantify calcium burden from non-gated CT scans.

    “AI algorithms can automate CAC detection and accurately quantify calcium burden,1213 potentially accelerating interpretation and improving reproducibility from non-gated CT scans.”

    From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [13]

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Authors
Gennari, A. G., Rossi, A., De Cecco, C. N., van Assen, M., Sartoretti, T., Giannopoulos, A. A., Schwyzer, M., Huellner, M. W., & Messerli, M.
Affiliations
  • University of Zurich (CH)
  • University Hospital Zurich (CH)
Corresponding author
Messerli, Michael
ORCID
Venue
The International Journal of Cardiovascular Imaging
Publisher
Springer Science and Business Media LLC
Published
2024-05-03
Volume
40
Issue
5
Pages
951-966
ISSN
  • 1569-5794 (ISSN-L)
  • 1875-8312
DOI
10.1007/s10554-024-03080-4
PMC
PMC11147943
OpenAlex
W4396619171
Cited by
16
References
123
Citations by year
2026: 5 · 2025: 7 · 2024: 4
FWCI
1.04
Type
journal article
Language
en
Open access
Licence
Locations
  • The International Journal of Cardiovascular Imaging (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by-nc-nd)
  • Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
  • Zurich Open Repository and Archive (University of Zurich)
Funders
  • Swiss Government Excellence Scholarship, Switzerland
  • Premio Nino ed Hansi Cominotti, Italy
  • Artificial Intelligence in Oncological Imaging Network (CRPP), University of Zurich
  • Bayer
  • Siemens
  • Xeos
  • Siemens Healthineers
  • Palatin Stiftung
  • Promedica Foundation
  • Max and Sophielene Iten-Kohaut Foundation
  • GE Healthcare
  • Alfred and Annemarie von Sick legacy
  • University of Zurich
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 › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
coronary artery calcium score, artificial intelligence, deep learning, atherosclerotic cardiovascular disease, cardiovascular risk stratification, non-gated CT, image reconstruction, coronary artery segmentation, lung nodule segmentation, primary prevention
MeSH terms
Computed Tomography Angiography, Deep Learning, Artificial Intelligence, Coronary Artery Disease, Coronary Vessels, Humans, Predictive Value of Tests, Prognosis, Radiographic Image Interpretation, Computer-Assisted, Severity of Illness Index, Reproducibility of Results, Coronary Angiography, Cardiac-Gated Imaging Techniques, Vascular Calcification

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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