Advancements in the application of deep learning for coronary artery calcification
Open accessAbstract
Coronary Artery Calcification (CAC) is a characteristic pathological alteration in the progression of coronary atherosclerosis and is considered an independent predictor of Major Adverse Cardiovascular Events (MACE). The distribution, pathological classification, and quantitative evaluation of CAC are pivotal factors influencing the incidence of MACE and guiding intracoronary interventions. Deep learning methods, a widely explored domain in artificial intelligence, achieve learning and understanding of big data by constructing multi-layer neural network models. This robust approach offers significant support for intelligent medical image diagnosis within clinical settings. Currently, deep learning methods have been applied to the identification and quantification of coronary artery calcification plaques, which not only improve diagnostic efficiency but also contribute to the early prevention and treatment of patients at moderate to low risk. This article reviews the progress of deep learning applications in coronary artery calcification to gain a comprehensive understanding of this field. • Coronary calcification predicts cardiovascular risk, requiring precise imaging for diagnosis and treatment guidance. • Deep learning enhances medical imaging analysis, boosting diagnostic accuracy and efficiency. • Deep learning assesses coronary calcification via segmentation, plaque detection, and score calculation.
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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 [15]
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 [15]
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Lee, J. O., Park, E., Park, D., & Lee, W. (2023) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd10040143 · Cited by 17 · see citing works
Wang, W., Yang, L., Wang, S., Wang, Q., & Xu, L. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-775 · Cited by 14 · see citing works
Klug, M., Shemesh, J., Green, M., Mayer, A., Kerpel, A., Konen, E., & Marom, E. M. (2022) · Clinical Radiology · doi:10.1016/j.crad.2022.03.005 · Cited by 11 · see citing works
Oh, H. S., Kim, T. H., Kim, J. W., Yang, J., Lee, H. S., Lee, J., & Park, C. H. (2023) · European Radiology · doi:10.1007/s00330-023-10390-z · Cited by 9 · see citing works
Mammography-based deep learning model for coronary artery calcification
Ahn, S.-I., Chang, Y., Kwon, R., Kang, J., Choi, J., Lim, G., Kwon, M.-R., Ryu, S., & Shin, J. (2023) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jead307 · Cited by 9 · see citing works
Yan, H., Liu, S., Zhang, J., Liu, J., & Li, T. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-1076 · Cited by 9 · see citing works
Muthusamy, C. D., & Murugesh, R. (2024) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-024-00473-2 · Cited by 6 · see citing works
Chen, H., Yan, S., Xie, M., Ye, Y., Ye, Y., Zhu, D., Su, L., & Huang, J. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106608 · Cited by 6 · see citing works
Zair, A. M., Cherfa, A. B., Cherfa, Y., & Belkhamsa, N. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02797-z · Cited by 5 · see citing works
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Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review
McDonald, V. J. F., Parsa, S., Mastrodicasa, D., Rodriguez, F., Langlotz, C., Maron, D. J., Sandhu, A. T., Dudum, R., & Jain, S. S. (2026) · BMJ Digital Health & AI · doi:10.1136/bmjdh-2026-000053
Zafar, J., Sharif, F., & Zafar, H. (2025) · Optics · doi:10.3390/opt6030038
- Authors
- Tang, K.-X., Wu, Y.-L., Shan, S.-K., & Yuan, L.-Q.
- Affiliations
- Second Xiangya Hospital of Central South University (CN)
- Corresponding author
- Yuan, Ling-Qing
- Venue
- Meta-Radiology
- Publisher
- Elsevier BV
- Published
- 2025-03
- Volume
- 3
- Issue
- 1
- Pages
- 100134
- ISSN
- 2950-1628 (ISSN-L)
- OpenAlex
- W4407172040
- Cited by
- 2
- References
- 91
- Citations by year
- 2026: 1 · 2025: 1
- FWCI
- 1.16
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Meta-Radiology (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key Research and Development Program of China (2023YFC2507600, 2021YFC2501701, 2021YFC2501701,2023YFC2507600)
- National Natural Science Foundation of China (82370892, 82070910, 82370892,82070910)
- 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 calcification, deep learning, coronary artery disease, major adverse cardiovascular events, plaque segmentation, cardiovascular risk prediction, medical image analysis
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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