ArticleEuropean heart journal. Cardiovascular Imaging2025
Automated proximal coronary artery calcium identification using artificial intelligence: advancing cardiovascular risk assessment.
Article in European heart journal. Cardiovascular Imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Hidden risk in normal myocardial perfusion scans: AI-detected proximal coronary calcium on CT attenuation maps improves prognosis.European journal of nuclear medicine and molecular imaging · 2026Article
- Age-Dependent Efficacy of Incidental Coronary Artery Calcium on Pre-CAG Chest CT: Potentially Missed Prevention Opportunities in Chinese Population.Journal of cardiovascular development and disease · 2026Article
- Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review.BMJ digital health & AI · 2026Article
- Cardiac CT in the era of artificial intelligence: precision imaging, treatment guidance and optimised risk stratification for coronary artery disease.Open heart · 2025Review
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Authors and funding
18 authors.
Funding
Abstract
aimsIdentification 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
resultsA 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).
conclusionThe 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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