ArticleEuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology2021
Training and validation of a deep learning architecture for the automatic analysis of coronary angiography.
Article in EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- A multiview video-text model detects obstructive coronary stenosis and predicts 1-year cardiovascular events from angiography.Nature cardiovascular research · 2026Article
- Article
- Deep Learning-Based Segmentation of Coronary Arteries and Stenosis Detection in X-Ray Coronary Angiography.JACC. Advances · 2025Article
- Artificial intelligence in coronary angiography: benchmarking the diagnostic accuracy of ChatGPT-4o against interventional cardiologists.Open heart · 2025Article
- A systematic review on deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction.European journal of radiology open · 2025Article
- Artificial Intelligence in Diagnosis of Heart Failure.Journal of the American Heart Association · 2025Review
- Enhancing quantitative coronary angiography (QCA) with advanced artificial intelligence: comparison with manual QCA and visual estimation.The international journal of cardiovascular imaging · 2025Article
- Automated stenosis estimation of coronary angiographies using end-to-end learning.The international journal of cardiovascular imaging · 2025Article
- Deep Learning-Based Detection of Malignant Bile Duct Stenosis in Fluoroscopy Images of Endoscopic Retrograde Cholangiopancreatography.Digestion · 2025Article
- Comprehensive Analysis of Cardiovascular Diseases: Symptoms, Diagnosis, and AI Innovations.Bioengineering (Basel, Switzerland) · 2024Review
- Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges.The Canadian journal of cardiology · 2024Review
- Advancements and applications of artificial intelligence in cardiovascular imaging: a comprehensive review.European heart journal. Imaging methods and practice · 2024Review
- Review
- Pretrained subtraction and segmentation model for coronary angiograms.Scientific reports · 2024Article
- Genetic factors, risk prediction and AI application of thrombotic diseases.Experimental hematology & oncology · 2024Review
- Old Habits Die Hard: Can AI Help Bring Coronary Angiography Into the 21st Century?JACC. Advances · 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
- Advancements in artificial intelligence-driven techniques for interventional cardiology.Cardiology journal · 2024Review
- One-Stage Detection without Segmentation for Multi-Type Coronary Lesions in Angiography Images Using Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
13 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIn recent years, the use of deep learning has become more commonplace in the biomedical field and its development will greatly assist clinical and imaging data interpretation. Most existing machine learning methods for coronary angiography analysis are limited to a single aspect.
aimsWe aimed to achieve an automatic and multimodal analysis to recognise and quantify coronary angiography, integrating multiple aspects, including the identification of coronary artery segments and the recognition of lesion morphology.
methodsA data set of 20,612 angiograms was retrospectively collected, among which 13,373 angiograms were labelled with coronary artery segments, and 7,239 were labelled with special lesion morphology. Trained and optimised by these labelled data, one network recognised 20 different segments of coronary arteries, while the other detected lesion morphology, including measures of lesion diameter stenosis as well as calcification, thrombosis, total occlusion, and dissection detections in an input angiogram.
resultsFor segment prediction, the recognition accuracy was 98.4%, and the recognition sensitivity was 85.2%. For detecting lesion morphologies including stenotic lesion, total occlusion, calcification, thrombosis, and dissection, the F1 scores were 0.829, 0.810, 0.802, 0.823, and 0.854, respectively. Only two seconds were needed for the automatic recognition.
conclusionsOur deep learning architecture automatically provides a coronary diagnostic map by integrating multiple aspects. This helps cardiologists to flag and diagnose lesion severity and morphology during the intervention.
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