ReviewThe international journal of cardiovascular imaging2024
Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes.
Review in The international journal of cardiovascular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed.
- Opportunistic Screening on Chest CT, From theAJR. American journal of roentgenology · 2026Review
- DINO-LG: Enhancing vision transformers with label guidance for coronary artery calcium detection.Medical & biological engineering & computing · 2026Article
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review.BMJ digital health & AI · 2026Article
- Impact of statins on progression of coronary artery calcium composition and density as assessed by noncontrast CT.The international journal of cardiovascular imaging · 2025Article
- Early detection of cardiovascular disease in chest population screening: challenges for a rapidly emerging cardiac CT application.The British journal of radiology · 2025Review
- Performance of fully automated deep-learning-based coronary artery calcium scoring in ECG-gated calcium CT and non-gated low-dose chest CT.European radiology · 2025Article
- Personalized Treatment of Patients with Coronary Artery Disease: The Value and Limitations of Predictive Models.Journal of cardiovascular development and disease · 2025Review
- Artificial intelligence in coronary artery calcification scoring: Current progress and future directions.Global cardiology science & practice · 2025Review
- Review
- Artificial intelligence-derived coronary artery calcium scoring saves time and achieves close to radiologist-level accuracy accuracy on routine ECG-gated CT.The international journal of cardiovascular imaging · 2025Article
- The Current Landscape of Artificial Intelligence in Imaging for Transcatheter Aortic Valve Replacement.Current radiology reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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.