Artificial Intelligence in Coronary Artery Calcium Scoring
Open accessAbstract
Cardiovascular disease (CVD), particularly coronary heart disease (CHD), is the leading cause of death in the US, with a high economic impact. Coronary artery calcium (CAC) is a known marker for CHD and a useful tool for estimating the risk of atherosclerotic cardiovascular disease (ASCVD). Although CACS is recommended for informing the decision to initiate statin therapy, the current standard requires a dedicated CT protocol, which is time-intensive and contributes to radiation exposure. Non-dedicated CT protocols can be taken advantage of to visualize calcium and reduce overall cost and radiation exposure; however, they mainly provide visual estimates of coronary calcium and have disadvantages such as motion artifacts. Artificial intelligence is a growing field involving software that independently performs human-level tasks, and is well suited for improving CACS efficiency and repurposing non-dedicated CT for calcium scoring. We present a review of the current studies on automated CACS across various CT protocols and discuss consideration points in clinical application and some barriers to implementation.
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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 [14]
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Experimental — accuracy not yet measuredReviews 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 [14]
The Abstract says this review covers automated CAC-scoring studies and barriers to clinical implementation, and Section 4 discusses implementation directly. The copy does not assess whether reviews generally emphasize algorithmic performance over clinical implementation; searches for prior reviews and that comparison found no such passage. Page numbers were not available in the copy. (LLM, conf=0.70)
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- Authors
- Aromiwura, A. A., & Kalra, D. K.
- Affiliations
- University of Louisville (US)
- Corresponding author
- Kalra, Dinesh K.
- ORCID
- Kalra, Dinesh K.
- Venue
- Journal of Clinical Medicine
- Publisher
- MDPI AG
- Published
- 2024-06-13
- Volume
- 13
- Issue
- 12
- Pages
- 3453
- ISSN
- 2077-0383 (ISSN-L)
- PMC
- PMC11205094
- OpenAlex
- W4399603140
- Cited by
- 11
- References
- 50
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 1
- FWCI
- 0.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Clinical Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- 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 scoring, artificial intelligence, radiation exposure, coronary artery disease, atherosclerotic cardiovascular disease, motion artifacts, CT angiography
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