ArticleScientific data2024
Dataset for Automatic Region-based Coronary Artery Disease Diagnostics Using X-Ray Angiography Images.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 70 citations in OpenAlex.
- Utility of artificial intelligence in predicting clinical outcomes of angiographic procedures: a comprehensive scoping review.Journal of cardiovascular imaging · 2026Review
- Vascular-aware mixture-of-experts with a texture-enhanced decoder for accurate X-ray vessel segmentation.BMC medical imaging · 2026Article
- IUM-hybrid model for enhanced CAD diagnosis using deep learning and VS Grad-CAM visualization.Scientific reports · 2026Article
- Coronary artery stenosis segmentation using U-Net architecture with customised loss function.Scientific reports · 2026Article
- A Machine Learning Driven Approach to Quantifying Coronary Artery Tortuosity.JACC. Advances · 2026Article
- Artificial Intelligence Powered Real-Time Coronary Stenosis Recognition and Quantification in Angiography.Journal of imaging informatics in medicine · 2026Article
- Angio-fusion net: dual-stream enhanced VGG16 attention U-Net for vessel morphology preservation in XCA segmentation.Frontiers in cardiovascular medicine · 2026Article
- A novel dynamic Optuna hybrid Harris Hawks Optimization approach for classification of CAD.PloS one · 2026Article
- Deep Learning-Based Segmentation of Coronary Arteries and Stenosis Detection in X-Ray Coronary Angiography.JACC. Advances · 2025Article
- A Synergistic Multi-Scale Attention and Composite Feature Extraction Network for Coronary Artery Segmentation.Bioengineering (Basel, Switzerland) · 2025Article
- A narrative review of foundation models for medical image segmentation: zero-shot performance evaluation on diverse modalities.Quantitative imaging in medicine and surgery · 2025Review
- X-ray Coronary Angiogram images and SYNTAX score to develop Machine-Learning algorithms for CHD Diagnosis.Scientific data · 2025Article
- Automated stenosis estimation of coronary angiographies using end-to-end learning.The international journal of cardiovascular imaging · 2025Article
- CoronaryDominance: Angiogram dataset for coronary dominance classification.Scientific data · 2025Article
- Efficient Extraction of Coronary Artery Vessels from Computed Tomography Angiography Images Using ResUnet and Vesselness.Bioengineering (Basel, Switzerland) · 2024Article
- Evaluation of stenoses using AI video models applied to coronary angiography.NPJ digital medicine · 2024Article
- Dataset for Automatic Region-based Coronary Artery Disease Diagnostics Using X-Ray Angiography Images.Scientific data · 2024Article
Corrections and comments
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Authors and funding
15 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
X-ray coronary angiography is the most common tool for the diagnosis and treatment of coronary artery disease. It involves the injection of contrast agents into coronary vessels using a catheter to highlight the coronary vessel structure. Typically, multiple 2D X-ray projections are recorded from different angles to improve visualization. Recent advances in the development of deep-learning-based tools promise significant improvement in diagnosing and treating coronary artery disease. However, the limited public availability of annotated X-ray coronary angiography image datasets presents a challenge for objective assessment and comparison of existing tools and the development of novel methods. To address this challenge, we introduce a novel ARCADE dataset with 2 objectives: coronary vessel classification and stenosis detection. Each objective contains 1500 expert-labeled X-ray coronary angiography images representing: i) coronary artery segments; and ii) the locations of stenotic plaques. These datasets will serve as a benchmark for developing new methods and assessing existing approaches for the automated diagnosis and risk assessment of coronary artery disease.
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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.