AIMC Topic: Coronary Angiography

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A multi-stage neural network approach for coronary 3D reconstruction from uncalibrated X-ray angiography images.

Scientific reports
We present a multi-stage neural network approach for 3D reconstruction of coronary artery trees from uncalibrated 2D X-ray angiography images. This method uses several binarized images from different angles to reconstruct a 3D coronary tree without a...

Deep Learning-Based Automated Labeling of Coronary Segments for Structured Reporting of Coronary Computed Tomography Angiography in Accordance With Society of Cardiovascular Computed Tomography Guidelines.

Journal of thoracic imaging
PURPOSE: To evaluate a novel deep learning (DL)-based automated coronary labeling approach for structured reporting of coronary artery disease according to the guidelines of the Society of Cardiovascular Computed Tomography (CT) on coronary CT angiog...

Diagnostic performance of a novel deep learning attenuation correction software for MPI using a cardio dedicated CZT camera. Experience in the clinical practice.

Revista espanola de medicina nuclear e imagen molecular
PURPOSE: To evaluate the diagnostic performance of a novel deep learning attenuation correction software (DLACS) for myocardial perfusion imaging (MPI) using a cadmium-zinc-telluride (CZT) cardio dedicated camera with invasive coronary angiography (I...

Coronary computed tomography angiographic detection of in-stent restenosis via deep learning reconstruction: a feasibility study.

European radiology
OBJECTIVES: Evaluation of in-stent restenosis (ISR), especially for small stents, remains challenging during computed tomography (CT) angiography. We used deep learning reconstruction to quantify stent strut thickness and lumen vessel diameter at the...

Improving detection of obstructive coronary artery disease with an artificial intelligence-enabled electrocardiogram algorithm.

Atherosclerosis
BACKGROUND AND AIMS: To evaluate the risk of coronary artery disease (CAD), the traditional approach involves assessing the patient's symptoms, traditional cardiovascular risk factors (CVRFs), and a 12-lead electrocardiogram (ECG). However, currently...

Deep learning-based scan range optimization can reduce radiation exposure in coronary CT angiography.

European radiology
OBJECTIVES: Cardiac computed tomography (CT) is essential in diagnosing coronary heart disease. However, a disadvantage is the associated radiation exposure to the patient which depends in part on the scan range. This study aimed to develop a deep ne...

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...

Leveraging ChatGPT to aid patient education on coronary angiogram.

Annals of the Academy of Medicine, Singapore
Natural-language artificial intelligence (AI) is a promising technological advancement poised to revolutionise the delivery of healthcare. We aim to explore the quality of ChatGPT in providing medical information regarding a common cardiology procedu...

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...