AIMC Topic: Coronary Stenosis

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Deep learning prediction of quantitative coronary angiography values using myocardial perfusion images with a CZT camera.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
PURPOSE: Evaluate the prediction of quantitative coronary angiography (QCA) values from MPI, by means of deep learning.

Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography.

European radiology
OBJECTIVES: Coronary computed tomography angiography (CCTA) has rapidly developed in the coronary artery disease (CAD) field. However, manual coronary artery tree segmentation and reconstruction are time-consuming and tedious. Deep learning algorithm...

Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion.

European journal of nuclear medicine and molecular imaging
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
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 ...

Evaluation of a deep learning model on coronary CT angiography for automatic stenosis detection.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to evaluate a deep-learning model (DLM) for classifying coronary arteries on coronary computed tomography -angiography (CCTA) using the Coronary Artery Disease-Reporting and Data System (CAD-RADS).

Digital Subtraction Angiography Image Features under the Deep Learning Algorithm in Cardiovascular Interventional Treatment and Nursing for Vascular Restenosis.

Computational and mathematical methods in medicine
The objective of this study was to explore the application value of digital subtraction angiography (DSA) images optimized by deep learning algorithms in vascular restenosis patients undergoing cardiovascular intervention and their nursing efficacy. ...

Deep learning powered coronary CT angiography for detecting obstructive coronary artery disease: The effect of reader experience, calcification and image quality.

European journal of radiology
OBJECTIVES: To investigate the effect of reader experience, calcification and image quality on the performance of deep learning (DL) powered coronary CT angiography (CCTA) in automatically detecting obstructive coronary artery disease (CAD) with inva...

Real-time coronary artery stenosis detection based on modern neural networks.

Scientific reports
Invasive coronary angiography remains the gold standard for diagnosing coronary artery disease, which may be complicated by both, patient-specific anatomy and image quality. Deep learning techniques aimed at detecting coronary artery stenoses may fac...