Acta radiologica (Stockholm, Sweden : 1987)
33423530
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).
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 ...
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...
European journal of nuclear medicine and molecular imaging
35194673
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...
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 ...
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...
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
35274211
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...
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.