ArticleNature communications2021
Deep convolutional neural networks to predict cardiovascular risk from computed tomography.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
106 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence in non-invasive cardiovascular imaging for coronary artery disease: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Coronary artery calcium score-emerging role for opportunistic screening on thoracic CT and novel applications.The British journal of radiology · 2026Review
- Deep Learning Optimizes the Diagnostic Performance of Coronary Computed Tomography Angiography in Heavily Calcified Coronary Plaques: A Chinese Multicenter Study.Reviews in cardiovascular medicine · 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
- Opportunistic Assessment of Coronary Artery Calcium Volume and Density From Non-Electrocardiogram-Gated Chest CT Using Artificial Intelligence: Prognostic Implications in a Screening Cohort.Korean journal of radiology · 2026Article
- 3D Convolutional Neural Network for Predicting Clinical Outcome from Coronary Computed Tomography Angiography in Patients with Suspected Coronary Artery Disease.Journal of imaging informatics in medicine · 2026Article
- Interpretable Semi-federated Learning for Multimodal Cardiac Imaging and Risk Stratification: A Privacy-Preserving Framework.Journal of imaging informatics in medicine · 2026Article
- Adnexal torsion diagnosis framework with CT-based adaptive preprocessing and deep neural networks.Scientific reports · 2026Article
- Machine learning-based prediction of ventilator therapeutic pressure for optimized CPAP titration.BMC medical informatics and decision making · 2026Article
- Aortic and Cardiac Structure From Routine CT Predict Cardiovascular Risk Beyond PREVENT and Coronary Calcium.JACC. Cardiovascular imaging · 2026Article
- Combined value of AIP and NHR for identifying obstructive coronary heart disease in premature cases with zero calcium.BMC cardiovascular disorders · 2026Article
- A deep-learning-based pipeline for automatic fusion of CT coronary angiogram and stress perfusion CMR.Medical physics · 2026Article
- DINO-LG: Enhancing vision transformers with label guidance for coronary artery calcium detection.Medical & biological engineering & computing · 2026Article
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- Prognostic value of deep learning-based coronary artery calcium score and quantitative pneumonia burden in patients hospitalized with COVID-19.BMC medical imaging · 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
- 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
- Artificial intelligence in cardiology: implications for healthcare outcomes.Frontiers in artificial intelligence · 2026Review
- Current State of Artificial Intelligence in Assessing Cardiac Function.Current cardiology reports · 2025Review
- Cardiac CT in the era of artificial intelligence: precision imaging, treatment guidance and optimised risk stratification for coronary artery disease.Open heart · 2025Review
46 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
23 authors.
Funding
Abstract
Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health.
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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.