ArticleJACC. Cardiovascular imaging2023
Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events.
Article in JACC. Cardiovascular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06379295 (Coronary Artery Calcification Assessed on PET Scanner), which is not on this map. Cited by 45 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.
Coronary Artery Calcification Assessed on PET Scanner (PET : Positron Emission Tomography)
Who cites it
45 citing papers in PubMed, 70 citations in OpenAlex.
- Incremental diagnostic value of artificial intelligence-derived coronary artery calcium inJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2025Trial
- Hidden risk in normal myocardial perfusion scans: AI-detected proximal coronary calcium on CT attenuation maps improves prognosis.European journal of nuclear medicine and molecular imaging · 2026Article
- Revisiting Perfusion in the Forgotten Right Ventricle: Artificial Intelligence-Enhanced Quantification on PET/CT.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Cross-Domain Knowledge Transfer from Expert-Annotated Gated CT via Synthetic Ungated CT Improves Coronary Artery Calcium Scoring on CT Attenuation Correction Scans.medRxiv : the preprint server for health sciences · 2026Article
- Mechanical dyssynchrony and septal-lateral perfusion heterogeneity predict adverse left ventricular remodelling beyond ECG-defined LBBB.European heart journal. Cardiovascular Imaging · 2026Article
- Opportunistic CT Attenuation Biomarkers of Anemia Are Associated With Impaired Myocardial Flow Reserve and Cardiovascular Outcomes.medRxiv : the preprint server for health sciences · 2026Article
- Virtual myocardial blood flow and flow reserve from static PET imaging using artificial intelligence.medRxiv : the preprint server for health sciences · 2026Article
- Coronary CT angiography evaluation with artificial intelligence for individualized medical treatment of atherosclerosis: a Consensus Statement from the QCI Study Group.Nature reviews. Cardiology · 2026Review
- Multicenter evaluation of interpretable AI for coronary artery disease diagnosis from PET biomarkers.NPJ digital medicine · 2026Article
- Mechanical Dyssynchrony and Perfusion Heterogeneity Predict Adverse LV Remodeling in Patients with and without LBBB.medRxiv : the preprint server for health sciences · 2026Article
- Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review.BMJ digital health & AI · 2026Article
- The application of artificial intelligence in the intersection of metabolic dysfunction-associated steatotic liver disease and cardiovascular diseases.Frontiers in immunology · 2026Review
- Age- and Sex-Adjusted Myocardial Flow Reserve Percentiles for Personalized Cardiovascular Risk Assessment.medRxiv : the preprint server for health sciences · 2025Article
- Impact of statins on progression of coronary artery calcium composition and density as assessed by noncontrast CT.The international journal of cardiovascular imaging · 2025Article
- Standard Visual and Ordinal Coronary Calcium Scoring on PET/CT: Agreement with Agatston Scoring and Prognostic Implications.Diagnostics (Basel, Switzerland) · 2025Article
- Ejection fraction quantification from ungated chest CT by AI.medRxiv : the preprint server for health sciences · 2025Article
- Integrating perfusion with AI-derived coronary calcium on CT attenuation scans to improve selection of low-risk studies for stress-only SPECT myocardial perfusion imaging.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2025Article
- The REgistry of Flow and Perfusion Imaging for Artificial Intelligence with positron emission tomography (REFINE PET):Rationale and design.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2025Article
- Deep Learning-Derived Cardiac Chamber Volumes and Mass From PET/CT Attenuation Scans: Associations With Myocardial Flow Reserve and Heart Failure.Circulation. Cardiovascular imaging · 2025Article
- Deep learning-quantified body composition from positron emission tomography/computed tomography and cardiovascular outcomes: a multicentre study.European heart journal · 2025Article
Corrections and comments
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Authors and funding
17 authors at 3 institutions in 3 countries.
Funding
Abstract
backgroundAssessment of coronary artery calcium (CAC) by computed tomographic (CT) imaging provides an accurate measure of atherosclerotic burden. CAC is also visible in computed tomographic attenuation correction (CTAC) scans, always acquired with cardiac positron emission tomographic (PET) imaging.
objectivesThe aim of this study was to develop a deep-learning (DL) model capable of fully automated CAC definition from PET CTAC scans.
methodsThe novel DL model, originally developed for video applications, was adapted to rapidly quantify CAC. The model was trained using 9,543 expert-annotated CT scans and was tested in 4,331 patients from an external cohort undergoing PET/CT imaging with major adverse cardiac events (MACEs) (follow-up 4.3 years), including same-day paired electrocardiographically gated CAC scans available in 2,737 patients. MACE risk stratification in 4 CAC score categories (0, 1-100, 101-400, and >400) was analyzed and CAC scores derived from electrocardiographically gated CT scans (standard scores) by expert observers were compared with automatic DL scores from CTAC scans.
resultsAutomatic DL scoring required <6 seconds per scan. DL CTAC scores provided stepwise increase in the risk for MACE across the CAC score categories (HR up to 3.2; P < 0.001). Net reclassification improvement of standard CAC scores over DL CTAC scores was nonsignificant (-0.02; 95% CI: -0.11 to 0.07). The negative predictive values for MACE of zero CAC with standard (85%) and DL CTAC (83%) CAC scores were similar (P = 0.19).
conclusionsDL CTAC scores predict cardiovascular risk similarly to standard CAC scores quantified manually by experienced operators from dedicated electrocardiographically gated CAC scans and can be obtained almost instantly, with no changes to PET/CT scanning protocol.
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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.