AIMC Topic: Coronary Artery Disease
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Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning.
International journal of cardiology
Mar 11, 2024
BACKGROUND: Quantitative coronary angiography (QCA) offers objective and reproducible measures of coronary lesions. However, significant inter- and intra-observer variability and time-consuming processes hinder the practical application of on-site QC...
Natural language processing to phenotype coronary computed tomography angiography: Development, validation, and initial results of a large multi-institution cohort.
Journal of cardiovascular computed tomography
Mar 7, 2024
AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging.
JACC. Cardiovascular imaging
Mar 6, 2024
BACKGROUND: Computed tomography attenuation correction (CTAC) improves perfusion quantification of hybrid myocardial perfusion imaging by correcting for attenuation artifacts. Artificial intelligence (AI) can automatically measure coronary artery cal...
Risk Assessment
Time Factors
Predictive Value of Tests
Humans
Coronary Angiography
Prognosis
Artificial Intelligence
Risk Factors
Retrospective Studies
Aged
Computed Tomography Angiography
Female
Male
Coronary Circulation
Reproducibility of Results
Middle Aged
Myocardial Perfusion Imaging
Radiographic Image Interpretation, Computer-Assisted
Single Photon Emission Computed Tomography Computed Tomography
Coronary Artery Disease
Coronary Vessels
Vascular Calcification
Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images.
Computer methods and programs in biomedicine
Mar 6, 2024
BACKGROUND AND OBJECTIVE: Heart failure (HF) is a multi-faceted and life-threatening syndrome that affects more than 64.3 million people worldwide. Current gold-standard screening technique, echocardiography, neglects cardiovascular information regul...
Comparison of machine learning-based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease.
European radiology
Feb 26, 2024
OBJECTIVES: To compare the diagnostic performance of machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and cardiac magnetic resonance (MR) perfusion mapping for functional assessment of coronary stenosis.
Diagnostic accuracy of artificial intelligence-enabled vectorcardiography versus myocardial perfusion SPECT in patients with suspected or known coronary heart disease.
Nuklearmedizin. Nuclear medicine
Feb 20, 2024
AIM: The present study evaluated with myocardial perfusion SPECT (MPS) the diagnostic accuracy of an artificial intelligence-enabled vectorcardiography system (Cardisiography, CSG) for detection of perfusion abnormalities.
Coronary artery disease evaluation during transcatheter aortic valve replacement work-up using photon-counting CT and artificial intelligence.
Diagnostic and interventional imaging
Feb 16, 2024
PURPOSE: The purpose of this study was to evaluate the capabilities of photon-counting (PC) CT combined with artificial intelligence-derived coronary computed tomography angiography (PC-CCTA) stenosis quantification and fractional flow reserve predic...
Machine learning-based coronary artery calcium score predicted from clinical variables as a prognostic indicator in patients referred for invasive coronary angiography.
European radiology
Feb 10, 2024
OBJECTIVES: Utilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification, and further assessed its prognostic significance.
An Anatomy- and Topology-Preserving Framework for Coronary Artery Segmentation.
IEEE transactions on medical imaging
Feb 2, 2024
Coronary artery segmentation is critical for coronary artery disease diagnosis but challenging due to its tortuous course with numerous small branches and inter-subject variations. Most existing studies ignore important anatomical information and vas...
Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images.
Scientific reports
Jan 20, 2024
This study aimed to design an end-to-end deep learning model for estimating the value of fractional flow reserve (FFR) using angiography images to classify left anterior descending (LAD) branch angiography images with average stenosis between 50 and ...
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