ArticleProceedings of SPIE--the International Society for Optical Engineering2019
Deep learning-based stenosis quantification from coronary CT Angiography.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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The trial behind it
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Who cites it
31 citing papers in PubMed.
- Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study.Cardiovascular research · 2020Trial
- Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects.Circulation. Cardiovascular imaging · 2020Trial
- 3D Convolutional Neural Network for Predicting Clinical Outcome from Coronary Computed Tomography Angiography in Patients with Suspected Coronary Artery Disease.Journal of imaging informatics in medicine · 2026Article
- AI-Driven multi-view learning from CCTA for myocardial infarction diagnosis.The international journal of cardiovascular imaging · 2026Article
- Automatic segmentation of coronary plaques in coronary CT angiography using neural networks.PloS one · 2026Article
- 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
- The role of artificial intelligence in coronary CT angiography.Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2024Review
- Coordinate-aware three-dimensional neural network for lower extremity arterial stenosis classification in CT angiography.Heliyon · 2024Article
- Application of Quantitative Assessment of Coronary Atherosclerosis by Coronary Computed Tomographic Angiography.Korean journal of radiology · 2024Review
- Diagnostic performance of deep learning to exclude coronary stenosis on CT angiography in TAVI patients.The international journal of cardiovascular imaging · 2024Article
- Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque Characterization.Reviews in cardiovascular medicine · 2024Review
- A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study.Frontiers in neurology · 2024Article
- Novel near-infrared spectroscopy-intravascular ultrasound-based deep-learning methodology for accurate coronary computed tomography plaque quantification and characterization.European heart journal open · 2023Article
- 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
- Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective.Frontiers in cardiovascular medicine · 2023Review
- Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis.Journal of medical artificial intelligence · 2022Article
- Distinguishing Type 1 from Type 2 Myocardial Infarction by Using CT Coronary Angiography.Radiology. Cardiothoracic imaging · 2022Article
- Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique.Diagnostics (Basel, Switzerland) · 2022Article
- Use of a deep-learning-based lumen extraction method to detect significant stenosis on coronary computed tomography angiography in patients with severe coronary calcification.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2022Article
- Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study.The Lancet. Digital health · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundCoronary computed tomography angiography (CTA) allows quantification of stenosis. However, such quantitative analysis is not part of clinical routine. We evaluated the feasibility of utilizing deep learning for quantifying coronary artery disease from CTA.
methodsA total of 716 diseased segments in 156 patients (66 ± 10 years) who underwent CTA were analyzed. Minimal luminal area (MLA), percent diameter stenosis (DS), and percent contrast density difference (CDD) were measured using semi-automated software (Autoplaque) by an expert reader. Using the expert annotations, deep learning was performed with convolutional neural networks using 10-fold cross-validation to segment CTA lumen and calcified plaque. MLA, DS and CDD computed using deep-learning-based approach was compared to expert reader measurements.
resultsThere was excellent correlation between the expert reader and deep learning for all quantitative measures (r=0.984 for MLA; r=0.957 for DS; and r=0.975 for CDD, p<0.001 for all). The expert reader and deep learning method was not significantly different for MLA (median 4.3 mm
conclusionsOur deep learning-based method allows quantitative measurement of coronary artery disease segments accurately from CTA and may enhance clinical reporting.
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Registered trials
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