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Coronary Artery Disease

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Deep learning-based coronary computed tomography analysis to predict functionally significant coronary artery stenosis.

Heart and vessels
Fractional flow reserve derived from coronary CT (FFR-CT) is a noninvasive physiological technique that has shown a good correlation with invasive FFR. However, the use of FFR-CT is restricted by strict application standards, and the diagnostic accur...

Deep learning-based motion correction algorithm for coronary CT angiography: Lowering the phase requirement for morphological and functional evaluation.

Journal of applied clinical medical physics
PURPOSE: To investigate the performance of a deep learning-based motion correction algorithm (MCA) at various cardiac phases of coronary computed tomography angiography (CCTA), and determine the extent to which it may allow for reliable morphological...

Deep learning model to predict exercise stress test results: Optimizing the diagnostic test selection strategy and reduce wastage in suspected coronary artery disease patients.

Computer methods and programs in biomedicine
BACKGROUND: Cardiac exercise stress testing (EST) offers a non-invasive way in the management of patients with suspected coronary artery disease (CAD). However, up to 30% EST results are either inconclusive or non-diagnostic, which results in signifi...

Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction.

Diagnostic and interventional imaging
Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has re...

Acute psychological stress-induced progenitor cell mobilization and cardiovascular events.

Journal of psychosomatic research
OBJECTIVE: Certain brain activation responses to psychological stress are associated with worse outcomes in CVD patients. We hypothesized that elevated acute psychological stress, manifesting as greater activity within neural centers for emotional re...

Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease.

BMC cardiovascular disorders
BACKGROUND: Information on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (DL) algorithm being proposed as an effective diagnostic tool for acute myocardial infarction (AMI). ...

Uncertainty aware training to improve deep learning model calibration for classification of cardiac MR images.

Medical image analysis
Quantifying uncertainty of predictions has been identified as one way to develop more trustworthy artificial intelligence (AI) models beyond conventional reporting of performance metrics. When considering their role in a clinical decision support set...