To explore the feasibility of a coronary angiography-based method developed with artificial intelligence which was able to automatically and quickly calculate coronary volumetric blood flow and coronary flow reserve (CFR), and explore the difference...
BACKGROUND: Invasive coronary angiography (ICA) is the gold standard in the diagnosis of coronary artery disease (CAD). Being invasive, it carries rare but serious risks including myocardial infarction, stroke, major bleeding, and death. A large prop...
Journal of computer assisted tomography
May 13, 2025
OBJECTIVE: The aim of the study was to evaluate the image quality of coronary computed tomography (CT) angiography (CCTA) in obese patients by using deep learning image reconstruction (DLIR) in comparison with adaptive statistical iterative reconstru...
Background Chest pain is a leading cause of outpatient and emergency department visits; advancements in artificial intelligence (AI) could improve coronary CT angiography (CCTA) workflows for these patients. Purpose To evaluate the performance of an ...
Deep learning methods are increasingly popular in assisting physicians with diagnosing coronary artery disease and reducing errors caused by subjective judgment. However, accessing and labeling medical imaging data, especially coronary angiography da...
Giornale italiano di cardiologia (2006)
May 1, 2025
In the past few years, computed tomography coronary angiography (CTCA) has rapidly become a widely used diagnostic tool in several clinical settings and is recommended by the European guidelines with a high degree of recommendation in patients with s...
European heart journal. Quality of care & clinical outcomes
May 1, 2025
BACKGROUND: Despite evidence supporting use of fractional flow reserve (FFR) and instantaneous waves-free ratio (iFR) to improve outcome of patients undergoing coronary angiography (CA) and percutaneous coronary intervention, such techniques are stil...
To evaluate deep learning-based calcium segmentation and quantification on ECG-gated cardiac CT scans compared with manual evaluation. Automated calcium quantification was performed using a neural network based on mask regions with convolutional neur...
Background Precise assessment of myocardial ischemia burden and cardiovascular risk stratification based on dynamic CT myocardial perfusion imaging (MPI) is lacking. Purpose To develop and validate a deep learning (DL) model for automated quantificat...
European heart journal. Cardiovascular Imaging
Mar 3, 2025
AIMS: Identification of proximal coronary artery calcium (CAC) may improve prediction of major adverse cardiac events (MACE) beyond the CAC score, particularly in patients with low CAC burden. We investigated whether the proximal CAC can be detected ...
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