ArticleNature communications2024
Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- Comparing incidence of heart failure in individuals with enlarged cardiac chambers versus diabetes.American journal of preventive cardiology · 2026Article
- AI-Powered Detection of Left Ventricular Myocardial Scar from Dual-Sequence CT: Global and Segmental Prediction Validated by CMR.Journal of imaging informatics in medicine · 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
- Longitudinal computed tomography body composition changes in patients receiving curative radiotherapy for non-small cell lung cancer.Physics and imaging in radiation oncology · 2026Article
- An artificial intelligence model to detect abnormal ejection fraction from non-contrast chest computed tomography: the CT-LVEF study.European heart journal. Digital health · 2026Article
- Synchronous wearable ultrasound for early detection of coronary and carotid artery comorbidity.Science advances · 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
- Aortic and Cardiac Structure From Routine CT Predict Cardiovascular Risk Beyond PREVENT and Coronary Calcium.JACC. Cardiovascular imaging · 2026Article
- Artificial intelligence-enabled cardiac volumetry for opportunistic screening of cardiomegaly on chest CT: clinical validation with echocardiography.Radiology advances · 2026Article
- Interpretable machine learning model based on routine metabolic laboratory indices to identify advanced chronic kidney disease.Frontiers in endocrinology · 2026Article
- Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review.BMJ digital health & AI · 2026Article
- Digital Therapeutics in Cardiovascular Healthcare: A Narrative Review.Current cardiology reports · 2025Review
- Review
- 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
- A Novel Concept of the "Standard Human" in the Assessment of Individual Total Heart Size: Lessons from Non-Contrast-Enhanced Cardiac CT Examinations.Diagnostics (Basel, Switzerland) · 2025Article
- Generative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.European journal of nuclear medicine and molecular imaging · 2025Article
- OPPORTUNISTIC ASSESSMENT OF CARDIOVASCULAR RISK USING AI-DERIVED STRUCTURAL AORTIC AND CARDIAC PHENOTYPES FROM NON-CONTRAST CHEST COMPUTED TOMOGRAPHY.medRxiv : the preprint server for health sciences · 2025Article
- Fully Automated Assessment of Cardiac Chamber Volumes and Myocardial Mass on Non-Contrast Chest CT with a Deep Learning Model: Validation Against Cardiac MR.Diagnostics (Basel, Switzerland) · 2024Article
- Application of donor predicted heart mass in heart transplant recipients with left ventricular assist device.JHLT open · 2024Article
- Artificial Intelligence-Driven Advances in Coronary Calcium Scoring: Expanding Preventive Cardiology.Cureus · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making.
Indexed as
Identifiers
What OpenQuestion holds
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.