ArticleNEJM AI2025
AI Opportunistic Coronary Calcium Screening at Veterans Affairs Hospitals.
Article in NEJM AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Coronary artery calcium score-emerging role for opportunistic screening on thoracic CT and novel applications.The British journal of radiology · 2026Review
- Opportunistic Coronary Artery Calcium Screening: Time for Clinical Implementation.Circulation · 2026Article
- Quantifying the impact of slice thickness on cardiovascular risk stratification in lung cancer screening: a multi-center "RESCUE" study.Quantitative imaging in medicine and surgery · 2026Article
- Clinical Metadata-Guided Limited-Angle CT Image Reconstruction.IEEE transactions on medical imaging · 2026Article
- Opportunistic Cardiovascular Risk Assessment Using Routine Head CT in the Emergency Department.Journal of the American College of Cardiology · 2026Article
- Combined value of AIP and NHR for identifying obstructive coronary heart disease in premature cases with zero calcium.BMC cardiovascular disorders · 2026Article
- Visual coronary calcium scoring to support opportunistic CAD screening: comparative evaluation of three established systems and introduction of a novel scoring system.International journal of cardiology. Heart & vasculature · 2026Article
- Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review.BMJ digital health & AI · 2026Article
- A Framework for Cross-Domain Generalization in Coronary Artery Calcium Scoring Across Gated and Non-Gated Computed Tomography.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Evaluation of different artificial intelligence-based coronary artery calcium scoring algorithms in nongated chest computed tomography.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2026Article
- Vision Foundry: A System for Training Foundational Vision AI Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
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17 authors.
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Abstract
backgroundCoronary artery calcium (CAC) is highly predictive of cardiovascular events. Although millions of chest computed tomography (CT) scans are performed annually in the United States, CAC is not routinely quantified from scans done for noncardiac purposes.
methodsWe developed a deep learning algorithm, AI-CAC, using 446 expert segmentations to automatically quantify CAC on noncontrast, nongated CT scans. Our study differs from prior works by utilizing imaging data from 98 medical centers across the Veterans Affairs national health care system, capturing extensive heterogeneity in imaging protocols, scanners, and patients. AI-CAC performance on nongated scans was compared against clinical standard electrocardiogram (ECG)-gated CAC scoring in 795 patients with paired gated scans within 1 year of their nongated scan. In addition, the model was tested on 8052 low-dose CTs (LDCTs) to simulate opportunistic CAC screening.
resultsNongated AI-CAC differentiated zero versus nonzero and less than 100 versus 100 or greater Agatston scores with accuracies of 89.4% (F1 0.93) and 87.3% (F1 0.89), respectively. Nongated AI-CAC was predictive of 10-year all-cause mortality (CAC 0 vs. >400 group: 25.4% vs. 60.2%, Cox hazard ratio 3.49; P<0.005), and composite first-time stroke, myocardial infarction, or death (CAC 0 vs. >400 group: 33.5% vs. 63.8%, Cox hazard ratio 3.00; P<0.005). In the LDCT dataset, 3091 out of 8052 (38.4%) individuals had AI-CAC scores >400. Four cardiologists qualitatively reviewed a random sample of the >400 AI-CAC LDCT patients and verified that 527 of the 531 (99.2%) would benefit from lipid-lowering therapy.
conclusionsThis nongated CT CAC algorithm was developed across a national health care system and shows strong performance in evaluation against paired gated CT scans. The model code and weights are available at https://github.com/Raffi-Hagopian/AI-CAC/. (Funded by the Veterans Affairs health care system.).
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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.