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Cited by 6Cardiac Imaging and DiagnosticsArtificial Intelligence in Healthcare and EducationCoronary Interventions and Diagnostics

Automated proximal coronary artery calcium identification using artificial intelligence: advancing cardiovascular risk assessment

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Zhou, Jianhang, Shanbhag, Aakash D, Han, Donghee, Marcinkiewicz, Anna M, Buchwald, Mikolaj, Miller, Robert J H, Killekar, Aditya, Manral, Nipun +10 more·2025·European Heart Journal - Cardiovascular Imaging
DOI 10.1093/ehjci/jeaf007PMC PMC11879235
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Abstract

Aims: Identification of proximal coronary artery calcium (CAC) may improve prediction of major adverse cardiac events (MACE) beyond the CAC score, particularly in patients with low CAC burden. We investigated whether the proximal CAC can be detected on gated cardiac CT and whether it provides prognostic significance with artificial intelligence (AI). Methods and results: A total of 2016 asymptomatic adults with baseline CAC CT scans from a single site were followed up for MACE for 14 years. An AI algorithm to classify CAC into proximal or not was created using expert annotations of total and proximal CAC and AI-derived cardiac structures. The algorithm was evaluated for prognostic significance on AI-derived CAC segmentation. In 303 subjects with expert annotations, the classification of proximal vs. non-proximal CAC reached an area under receiver operating curve of 0.93 [95% confidence interval (CI) 0.91–0.95]. For prognostic evaluation, in an additional 588 subjects with mild AI-derived CAC scores (CAC score 1–99), the AI proximal involvement was associated with worse MACE-free survival (P = 0.008) and higher risk of MACE when adjusting for CAC score alone [hazard ratio (HR) 2.28, 95% CI 1.16–4.48, P = 0.02] or CAC score and clinical risk factors (HR 2.12, 95% CI 1.03–4.36, P = 0.04). Conclusion: The AI algorithm could identify proximal CAC on CAC CT. The proximal location had modest prognostic significance in subjects with mild CAC scores. The AI identification of proximal CAC can be integrated into automatic CAC scoring and improves the risk prediction of CAC CT.

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  • AI-CAC models generally relied on traditional categorical CAC (e.g., Agatston score risk categories of 0, 1-99, 100-399, ≥400) along with clinical features as core inputs, although several approaches extended quantification to individual coronary vessels or segments.

    “AI-CAC models generally relied on traditional categorical CAC (eg, Agatston score risk categories of 0, 1–99, 100–399, ≥400) along with clinical features as core inputs, although several approaches extended quantification to the level of individual coronary vessels or segments to capture regional burden and distribution.3334”

    From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [34]

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Authors
Zhou, J., Shanbhag, A. D., Han, D., Marcinkiewicz, A. M., Buchwald, M., Miller, R. J. H., Killekar, A., Manral, N., Grodecki, K., Geers, J., Pieszko, K., Yi, J., Zhang, W., Waechter, P., Gransar, H., Dey, D., Berman, D. S., & Slomka, P. J.
Affiliations
  • Cedars-Sinai Medical Center (US)
  • University of Southern California (US)
  • Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center , 6500 Wilshire Boulevard, Los Angeles, CA 90048 ,
Corresponding author
Slomka, Piotr J
ORCID
Venue
European Heart Journal - Cardiovascular Imaging
Publisher
Oxford University Press (OUP)
Published
2025-01-16
Volume
26
Issue
3
Pages
471-480
ISSN
  • 2047-2404 (ISSN-L)
  • 2047-2412
DOI
10.1093/ehjci/jeaf007
PMC
PMC11879235
OpenAlex
W4406463128
Cited by
6
References
3
Citations by year
2026: 3 · 2025: 3
FWCI
3.46
Type
journal article
Language
en
Open access
Licence
academic.oup.com/pages/standard-publication-reuse-rights (vor)
Locations
  • European Heart Journal - Cardiovascular Imaging (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion)
  • VUBIR (Vrije Universiteit Brussel) (submittedVersion)
Funders
  • National Heart, Lung, and Blood Institute
  • National Institutes of Health
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, artificial intelligence, major adverse cardiac events, cardiovascular risk prediction
MeSH terms
Computed Tomography Angiography, Adult, Aged, Algorithms, Artificial Intelligence, Coronary Artery Disease, Female, Follow-Up Studies, Humans, Male, Middle Aged, Prognosis, Retrospective Studies, ROC Curve, Severity of Illness Index, Tomography, X-Ray Computed, Cohort Studies, Coronary Angiography, Risk Assessment, Vascular Calcification

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