ArticleRadiology. Cardiothoracic imaging2021
Deep Learning-Quantified Calcium Scores for Automatic Cardiovascular Mortality Prediction at Lung Screening Low-Dose CT.
Article in Radiology. Cardiothoracic imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 22 citations in OpenAlex.
- 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
- Applying artificial intelligence to ensure high quality and equitable lung cancer screening.Translational lung cancer research · 2026Review
- Artificial intelligence for detecting acute heart failure on chest CT: prospective clinical proof-of-concept validation.European radiology experimental · 2026Article
- Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review.BMJ digital health & AI · 2026Article
- Early detection of cardiovascular disease in chest population screening: challenges for a rapidly emerging cardiac CT application.The British journal of radiology · 2025Review
- Artificial intelligence-derived coronary artery calcium scoring saves time and achieves close to radiologist-level accuracy accuracy on routine ECG-gated CT.The international journal of cardiovascular imaging · 2025Article
- Calcium scoring during 18F-FDG PET/CT in cancer indications: Improving cardiovascular risk stratification and prevention.PloS one · 2025Article
- Use of AI in Cardiac CT and MRI: A Scientific Statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA.Radiology · 2025Review
- Deep learning to predict cardiovascular mortality from aortic disease in heavy smokers.NPJ cardiovascular health · 2024Article
- Digital health innovation and artificial intelligence in cardiovascular care: a case-based review.NPJ cardiovascular health · 2024Review
- Artificial intelligence-enhanced patient evaluation: bridging art and science.European heart journal · 2024Review
- AI for Multistructure Incidental Findings and Mortality Prediction at Chest CT in Lung Cancer Screening.Radiology · 2024Article
- Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review.Journal of the American College of Cardiology · 2024Review
- Automated vessel-specific coronary artery calcification quantification with deep learning in a large multi-centre registry.European heart journal. Cardiovascular Imaging · 2024Article
- Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography.Nature communications · 2024Article
- Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis.Reviews in cardiovascular medicine · 2024Review
- Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events.JACC. Cardiovascular imaging · 2023Article
- Role and progress of artificial intelligence in radiodiagnosing vascular calcification: a narrative review.Annals of translational medicine · 2023Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo examine the prognostic value of location-specific arterial calcification quantities at lung screening low-dose CT for the prediction of cardiovascular disease (CVD) mortality. MATERIALS AND
methodsThis retrospective study included 5564 participants who underwent low-dose CT from the National Lung Screening Trial between August 2002 and April 2004, who were followed until December 2009. A deep learning network was trained to quantify six types of vascular calcification: thoracic aorta calcification (TAC); aortic and mitral valve calcification; and coronary artery calcification (CAC) of the left main, the left anterior descending, and the right coronary artery. TAC and CAC were determined in six evenly distributed slabs spatially aligned among chest CT images. CVD mortality prediction was performed with multivariable logistic regression using least absolute shrinkage and selection operator. The methods were compared with semiautomatic baseline prediction using self-reported participant characteristics, such as age, history of smoking, and history of illness. Statistical significance between the prediction models was tested using the nonparametric DeLong test.
resultsThe prediction model was trained with data from 4451 participants (median age, 61 years; 37.9% women) and then tested on data from 1113 participants (median age, 61 years; 37.9% women). The prediction model using calcium scores achieved a C statistic of 0.74 (95% CI: 0.69, 0.79), and it outperformed the baseline model using only participant characteristics (C statistic, 0.69;
conclusionFive-year CVD mortality prediction using automatically extracted image-based features is feasible at lung screening low-dose CT.© RSNA, 2021.
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Registered trials
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