ArticleScientific reports2021
Real-time coronary artery stenosis detection based on modern neural networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- A Bayesian-Optimized Weighted Fuzzy Rank-Based Ensemble for Coronary Stenosis Detection in X-ray Angiography.Journal of imaging informatics in medicine · 2026Article
- Introduction and validation of OSCAR-optimal stent choice algorithm.CVIR endovascular · 2026Article
- Artificial Intelligence Powered Real-Time Coronary Stenosis Recognition and Quantification in Angiography.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence in the cath lab: Bridging predictive models and real-time procedural decision support.EXCLI journal · 2026Review
- A novel dynamic Optuna hybrid Harris Hawks Optimization approach for classification of CAD.PloS one · 2026Article
- Cardiologist user experience of artificial intelligence-based quantitative coronary angiography.Cardiovascular diagnosis and therapy · 2025Article
- Deep Learning-Based Segmentation of Coronary Arteries and Stenosis Detection in X-Ray Coronary Angiography.JACC. Advances · 2025Article
- Pre-trained Artificial Intelligence Models in the Prediction and Classification of Atherosclerotic Cardiovascular Disease.The Eurasian journal of medicine · 2025Article
- Essential Concepts in Artificial Intelligence: A Guide for Pediatric Providers.Children (Basel, Switzerland) · 2025Review
- A multi-model deep learning approach for the identification of coronary artery calcifications within 2D coronary angiography images.International journal of computer assisted radiology and surgery · 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
- CDKD-w+: A Keyframe Recognition Method for Coronary Digital Subtraction Angiography Video Sequence Based on w+ Space Encoding.Sensors (Basel, Switzerland) · 2025Article
- LT-YOLO: long-term temporal enhanced YOLO for stenosis detection on invasive coronary angiography.Frontiers in molecular biosciences · 2025Article
- DCA-YOLOv8: A Novel Framework Combined with AICI Loss Function for Coronary Artery Stenosis Detection.Sensors (Basel, Switzerland) · 2024Article
- Effective descriptor extraction strategies for correspondence matching in coronary angiography images.Scientific reports · 2024Article
- How AI and Robotics Will Advance Interventional Radiology: Narrative Review and Future Perspectives.Diagnostics (Basel, Switzerland) · 2024Review
- Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images.Scientific reports · 2024Article
- Advancements in artificial intelligence-driven techniques for interventional cardiology.Cardiology journal · 2024Review
- Explaining decisions of a light-weight deep neural network for real-time coronary artery disease classification in magnetic resonance imaging.Journal of real-time image processing · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Invasive coronary angiography remains the gold standard for diagnosing coronary artery disease, which may be complicated by both, patient-specific anatomy and image quality. Deep learning techniques aimed at detecting coronary artery stenoses may facilitate the diagnosis. However, previous studies have failed to achieve superior accuracy and performance for real-time labeling. Our study is aimed at confirming the feasibility of real-time coronary artery stenosis detection using deep learning methods. To reach this goal we trained and tested eight promising detectors based on different neural network architectures (MobileNet, ResNet-50, ResNet-101, Inception ResNet, NASNet) using clinical angiography data of 100 patients. Three neural networks have demonstrated superior results. The network based on Faster-RCNN Inception ResNet V2 is the most accurate and it achieved the mean Average Precision of 0.95, F1-score 0.96 and the slowest prediction rate of 3 fps on the validation subset. The relatively lightweight SSD MobileNet V2 network proved itself as the fastest one with a low mAP of 0.83, F1-score of 0.80 and a mean prediction rate of 38 fps. The model based on RFCN ResNet-101 V2 has demonstrated an optimal accuracy-to-speed ratio. Its mAP makes up 0.94, F1-score 0.96 while the prediction speed is 10 fps. The resultant performance-accuracy balance of the modern neural networks has confirmed the feasibility of real-time coronary artery stenosis detection supporting the decision-making process of the Heart Team interpreting coronary angiography findings.
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