AIMC Topic: Fractional Flow Reserve, Myocardial
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The impact of deep learning reconstruction on image quality and coronary CT angiography-derived fractional flow reserve values.
European radiology
May 21, 2022
OBJECTIVES: To explore the impact of deep learning reconstruction (DLR) on image quality and machine learning-based coronary CT angiography (CTA)-derived fractional flow reserve (CT-FFR) values.
Multi-task Deep Learning of Myocardial Blood Flow and Cardiovascular Risk Traits from PET Myocardial Perfusion Imaging.
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Mar 10, 2022
BACKGROUND: Advanced cardiac imaging with positron emission tomography (PET) is a powerful tool for the evaluation of known or suspected cardiovascular disease. Deep learning (DL) offers the possibility to abstract highly complex patterns to optimize...
Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion.
European journal of nuclear medicine and molecular imaging
Feb 23, 2022
PURPOSE: To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically significant coronary artery disease (CAD) by using a rest dataset of myocardial computed tomography perfusion (CTP) as compared to invasive eval...
Diagnostic performance of deep learning and computational fluid dynamics-based instantaneous wave-free ratio derived from computed tomography angiography.
BMC cardiovascular disorders
Feb 5, 2022
BACKGROUND AND OBJECTIVES: Both fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) are widely used to evaluate ischemia-causing coronary lesions. A new method of CT-iFR, namely AccuiFRct, for calculating iFR based on deep learning ...
Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia.
BMC cardiovascular disorders
Feb 5, 2022
BACKGROUND: Machine-Learning Computed Tomography-Based Fractional Flow Reserve (CT-FFR) is a novel tool for the assessment of hemodynamic relevance of coronary artery stenoses. We examined the diagnostic performance of CT-FFR compared to stress perfu...
Deep Learning Algorithm Predicts Angiographic Coronary Artery Disease in Stable Patients Using Only a Standard 12-Lead Electrocardiogram.
The Canadian journal of cardiology
Aug 20, 2021
BACKGROUND: Current electrocardiogram analysis algorithms cannot predict the presence of coronary artery disease (CAD), especially in stable patients. This study assessed the ability of an artificial intelligence algorithm (ECGio; HEARTio Inc, Pittsb...
A 2-year investigation of the impact of the computed tomography-derived fractional flow reserve calculated using a deep learning algorithm on routine decision-making for coronary artery disease management.
European radiology
Feb 25, 2021
OBJECTIVE: This study aims to investigate the safety and feasibility of using a deep learning algorithm to calculate computed tomography angiography-based fractional flow reserve (DL-FFRCT) as an alternative to invasive coronary angiography (ICA) in ...
Additional value of deep learning computed tomographic angiography-based fractional flow reserve in detecting coronary stenosis and predicting outcomes.
Acta radiologica (Stockholm, Sweden : 1987)
Jan 10, 2021
BACKGROUND: Deep learning (DL) has achieved great success in medical imaging and could be utilized for the non-invasive calculation of fractional flow reserve (FFR) from coronary computed tomographic angiography (CCTA) (CT-FFR).
Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis.
BioMed research international
Dec 16, 2020
Cardiac computed tomography angiography (CCTA) is widely used as a diagnostic tool for evaluation of coronary artery disease (CAD). Despite the excellent capability to rule-out CAD, CCTA may overestimate the degree of stenosis; furthermore, CCTA anal...
Coronary Stenosis
Fractional Flow Reserve, Myocardial
Image Processing, Computer-Assisted
Phenotype
Coronary Artery Disease
Humans
Myocardial Ischemia
Prognosis
Calcium
Artificial Intelligence
Risk
Middle Aged
Coronary Vessels
Machine Learning
Plaque, Atherosclerotic
Diagnosis, Computer-Assisted
Algorithms
Computed Tomography Angiography
Early Feasibility of Automated Artificial Intelligence Angiography Based Fractional Flow Reserve Estimation.
The American journal of cardiology
Oct 13, 2020
Despite the evidence of improved patients' outcome, fractional flow reserve (FFR) is underused in current everyday practice. We aimed to evaluate the feasibility of a novel automated artificial intelligence angiography-based FFR software (AutocathFFR...
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