ArticleiScience2023
Chest radiography deep radiomics-enabled aortic arch calcification interpretation across different populations.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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Who cites it
5 citing papers in PubMed.
- Deep learning-based automated segmentation and quantification of aortic arch calcification at chest radiograph.BMC geriatrics · 2026Article
- Artificial Intelligence for Detecting Aortic Arch Calcification on Chest Radiographs: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Femoral artery calcification predicts hip fracture in maintenance hemodialysis patients.Archives of osteoporosis · 2025Article
- Vascular Calcification Heterogeneity from Bench to Bedside: Implications for Manifestations, Pathogenesis, and Treatment Considerations.Aging and disease · 2024Review
- Nephrology: a flourishing field with plentiful emerging topics.Frontiers in medicine · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Earlier detection of aortic calcification can facilitate subsequent cardiovascular care planning. Opportunistic screening based on plain chest radiography is potentially feasible in various population. We used multiple deep convolutional neural network (CNN) transfer learning by fine-tuning pre-trained models followed by ensemble technique for aortic arch calcification on chest radiographs from a derivation and two external databases with distinct features. Our ensemble approach achieved 84.12% precision, 84.70% recall, and an area under the receiver-operating-characteristic curve (AUC) of 0.85 in the general population/older adult's dataset. We also obtained 87.5% precision, 85.56% recall, and an AUC of 0.86 in the pre-end-stage kidney disease (pre-ESKD) cohort. We identified discriminative regions for identifying aortic arch calcification between patients without and with pre-ESKD. These findings are expected to optimize cardiovascular risk prediction if our model is incorporated into the process of routine care.
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Registered trials
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