ArticleThe British journal of radiology2020
Diagnostic performance of deep learning-based vascular extraction and stenosis detection technique for coronary artery disease.
Article in The British journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Conjunctival Vascular Metrics Using Automated Vessel Detection from Slit Lamp Images for Hyperemia Severity Assessment.Diagnostics (Basel, Switzerland) · 2026Article
- Comparative assessment of non-invasive imaging methods for coronary artery disease: coronary computed tomography angiography, computed tomography-derived fractional flow reserve and instantaneous wave-free ratio computed tomography.The international journal of cardiovascular imaging · 2026Review
- CCTA-derived trans-lesion fractional flow reserve gradient for predicting cardiovascular events after PCI: a prospective observational study.BMC medical imaging · 2026Observational
- Role of Computed Tomography and Other Non-Invasive and Invasive Imaging Modalities in Cardiac Allograft Vasculopathy.Journal of cardiovascular development and disease · 2025Review
- Burnout crisis in Chinese radiology: will artificial intelligence help?European radiology · 2025Article
- Application of one heartbeat acquisition with motion correction algorithm in CCTA of patients with atrial fibrillation: evaluation of coronary artery stenoses using artificial intelligence assisted diagnostic system.Frontiers in radiology · 2025Article
- Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges.The Canadian journal of cardiology · 2024Review
- Automated detection and classification of coronary atherosclerotic plaques on coronary CT angiography using deep learning algorithm.Quantitative imaging in medicine and surgery · 2024Article
- Deep learning-based coronary artery calcium score to predict coronary artery disease in type 2 diabetes mellitus.Heliyon · 2024Article
- Lesion-specific pericoronary adipose tissue CT attenuation improves risk prediction of major adverse cardiovascular events in coronary artery disease.The British journal of radiology · 2024Article
- Artificial intelligence-based opportunistic detection of coronary artery stenosis on aortic computed tomography angiography in emergency department patients with acute chest pain.European heart journal open · 2023Article
- Validation of the commercial coronary computed tomographic angiography artificial intelligence for coronary artery stenosis: a cross-sectional study.Quantitative imaging in medicine and surgery · 2023Article
- Developing a Deep-Learning-Based Coronary Artery Disease Detection Technique Using Computer Tomography Images.Diagnostics (Basel, Switzerland) · 2023Article
- Diagnostic performance of deep learning-based vessel extraction and stenosis detection on coronary computed tomography angiography for coronary artery disease: a multi-reader multi-case study.La Radiologia medica · 2023Article
- Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis.Journal of medical artificial intelligence · 2022Article
- Do plaque-related factors affect the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system? Comparison with invasive coronary angiography.European radiology · 2022Article
- Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review.Frontiers in cardiovascular medicine · 2022Article
- Artificial Intelligence in Coronary CT Angiography: Current Status and Future Prospects.Frontiers in cardiovascular medicine · 2022Review
- Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study.Frontiers in cardiovascular medicine · 2021Article
- Imaging patients with stable chest pain special feature: introductory editorial.The British journal of radiology · 2020Article
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10 authors.
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Abstract
objectiveTo investigate the diagnostic performance of deep learning (DL)-based vascular extraction and stenosis detection technology in assessing coronary artery disease (CAD).
methodsThe diagnostic performance of DL technology was evaluated by retrospective analysis of coronary computed tomography angiography in 124 suspected CAD patients, using invasive coronary angiography as reference standard. Lumen diameter stenosis ≥50% was considered obstructive, and the diagnostic performances were evaluated at per-patient, per-vessel and per-segment levels. The diagnostic performances between DL model and reader model were compared by the areas under the receiver operating characteristics curves (AUCs).
resultsIn patient-based analysis, AUC of 0.78 was obtained by DL model to detect obstructive CAD [sensitivity of 94%, specificity of 63%, positive predictive value of 94%, and negative predictive value of 59%], While AUC by reader model was 0.74 (sensitivity of 97%, specificity of 50%, positive predictive value of 93%, negative predictive value of 73%). In vessel-based analysis, the AUCs of DL model and reader model were 0.87 and 0.89 respectively. In segment-based analysis, the AUCs of 0.84 and 0.89 were obtained by DL model and reader model respectively. It took 0.47 min to analyze all segments per patient by DL model, which is significantly less than reader model (29.65 min) (
conclusionThe DL technology can accurately and effectively identify obstructive CAD, with less time-consuming, and it could be a reliable diagnostic tool to detect CAD. ADVANCES IN KNOWLEDGE: The DL technology has valuable prospect with the diagnostic ability to detect CAD.
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