ArticleNPJ cardiovascular health2024
Deep learning to predict cardiovascular mortality from aortic disease in heavy smokers.
Article in NPJ cardiovascular health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Automated AI-Based Aortic Measurements From Attenuation Correction CT as an Adjunctive Cardiovascular Risk Biomarker: An International Multicenter Study.Circulation. Cardiovascular imaging · 2026Article
- Validation of a Deep Learning U-Net Algorithm for Multistructure Segmentation of Infrarenal Abdominal Aortic Aneurysms including Lumen, Thrombus, and Calcifications.EJVES vascular forum · 2026Article
- Advancing global cardiovascular research and clinical translation.NPJ cardiovascular health · 2025Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aortic angiopathy is a common manifestation of cardiovascular disease (CVD) and may serve as a surrogate marker of CVD burden. While the maximum aortic diameter is the primary prognostic measure, the potential of other features to improve risk prediction remains uncertain. This study developed a deep learning framework to automatically quantify thoracic aortic disease features and assessed their prognostic value in predicting CVD mortality among heavy smokers. Using non-contrast chest CTs from the National Lung Screening Trial (NLST), aortic features quantified included maximum diameter, volume, and calcification burden. Among 24,770 participants, 440 CVD deaths occurred over a mean 6.3-year follow-up. Aortic calcifications and volume were independently associated with CVD mortality, even after adjusting for traditional risk factors and coronary artery calcifications. These findings suggest that deep learning-derived aortic features could improve CVD risk prediction in high-risk populations, enabling more personalized prevention strategies.
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Registered trials
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