ArticleEuropean radiology2023
Evaluation of an artificial intelligence coronary artery calcium scoring model from computed tomography.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 43 citations in OpenAlex.
- 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
- Deep learning predicts stent implantation in borderline coronary lesions from angiography.NPJ digital medicine · 2026Article
- DINO-LG: Enhancing vision transformers with label guidance for coronary artery calcium detection.Medical & biological engineering & computing · 2026Article
- Beyond Traditional Risk Calculators: The Expanding Role of Coronary Artery Calcium Scoring in Preventive Cardiology.Cureus · 2025Review
- Review
- Review
- Artificial Intelligence in Cardiovascular Imaging and Interventional Cardiology: Emerging Trends and Clinical Implications.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Artificial Intelligence in Ischemic Heart Disease Prevention.Current cardiology reports · 2025Review
- Revolutionizing Cardiology: The Role of Artificial Intelligence in Echocardiography.Journal of clinical medicine · 2025Review
- Echocardiographic video-driven multi-task learning model for coronary artery disease diagnosis and severity grading.Frontiers in bioengineering and biotechnology · 2025Article
- Advancements in Cardiac CT Imaging: The Era of Artificial Intelligence.Echocardiography (Mount Kisco, N.Y.) · 2024Review
- Artificial Intelligence-Driven Advances in Coronary Calcium Scoring: Expanding Preventive Cardiology.Cureus · 2024Review
- Automatic identification of coronary stent in coronary calcium scoring CT using deep learning.Scientific reports · 2024Article
- Automated segment-level coronary artery calcium scoring on non-contrast CT: a multi-task deep-learning approach.Insights into imaging · 2024Article
- Charting the Unseen: How Non-Invasive Imaging Could Redefine Cardiovascular Prevention.Journal of cardiovascular development and disease · 2024Review
- Revolutionizing Cardiac Imaging: A Scoping Review of Artificial Intelligence in Echocardiography, CTA, and Cardiac MRI.Journal of imaging · 2024Article
- Artificial Intelligence and Health Inequities in Dietary Interventions on Atherosclerosis: A Narrative Review.Nutrients · 2024Review
- Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?Current atherosclerosis reports · 2024Review
- Artificial Intelligence in Coronary Artery Calcium Scoring.Journal of clinical medicine · 2024Review
- Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes.The international journal of cardiovascular imaging · 2024Review
Corrections and comments
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Authors and funding
13 authors at 6 institutions in 3 countries.
Funding
Abstract
objectivesCoronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC.
methodsFully convolutional neural networks for automated CAC scoring were developed and trained on 2439 cardiac CT scans and validated using 771 scans. The model was tested on an independent set of 1849 cardiac CT scans. Agatston CAC scores were further categorised into five risk categories (0, 1-10, 11-100, 101-400, and > 400). Automated scores were compared to the manual reference standard (level 3 expert readers).
resultsOf 1849 scans used for model testing (mean age 55.7 ± 10.5 years, 49% males), the automated model detected the presence of CAC in 867 (47%) scans compared with 815 (44%) by human readers (p = 0.09). CAC scores from the model correlated very strongly with the manual score (Spearman's r = 0.90, 95% confidence interval [CI] 0.89-0.91, p < 0.001 and intraclass correlation coefficient = 0.98, 95% CI 0.98-0.99, p < 0.001). The model classified 1646 (89%) into the same risk category as human observers. The Bland-Altman analysis demonstrated little difference (1.69, 95% limits of agreement: -41.22, 44.60) and there was almost excellent agreement (Cohen's κ = 0.90, 95% CI 0.88-0.91, p < 0.001). Model analysis time was 13.1 ± 3.2 s/scan.
conclusionsThis artificial intelligence-based fully automated CAC scoring model shows high accuracy and low analysis times. Its potential to optimise clinical workflow efficiency and patient outcomes requires evaluation. KEY POINTS: • Coronary artery calcium (CAC) scores are traditionally assessed using cardiac computed tomography and require manual input by human operators to identify calcified lesions. • A novel artificial intelligence (AI)-based model for fully automated CAC scoring was developed and tested on an independent dataset of computed tomography scans, showing very high levels of correlation and agreement with manual measurements as a reference standard. • AI has the potential to assist in the identification and quantification of CAC, thereby reducing the time required for human analysis.
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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.