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Coronary Artery Disease

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Automated identification of myocardial perfusion defects in dynamic cardiac computed tomography using deep learning.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: The purpose of this study was to develop and evaluate deep convolutional neural network (CNN) models for quantifying myocardial blood flow (MBF) as well as for identifying myocardial perfusion defects in dynamic cardiac computed tomography (...

Deep Learning-Based Acceleration of Compressed Sensing for Noncontrast-Enhanced Coronary Magnetic Resonance Angiography in Patients With Suspected Coronary Artery Disease.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The clinical application of coronary MR angiography (MRA) remains limited due to its long acquisition time and often unsatisfactory image quality. A compressed sensing artificial intelligence (CSAI) framework was recently introduced to ov...

Mastering the Learning Curve for Robotic-Assisted Coronary Artery Bypass Surgery.

The Annals of thoracic surgery
BACKGROUND: Previous studies have evaluated the learning curve to achieve competency in robotic-assisted coronary artery bypass grafting (CABG) but have not identified thresholds for mastery. Robotic-assisted CABG is a minimally invasive alternative ...

A Nationwide Study of Clinical Outcomes After Robot-Assisted Coronary Artery Bypass Surgery and Hybrid Revascularization in the Netherlands.

Innovations (Philadelphia, Pa.)
OBJECTIVE: Robot-assisted minimally invasive direct coronary artery bypass (RA-MIDCAB) surgery and hybrid coronary revascularization (HCR) are minimally invasive alternative strategies to conventional coronary artery bypass surgery in patients with i...

Diagnostic performance of deep learning-based vessel extraction and stenosis detection on coronary computed tomography angiography for coronary artery disease: a multi-reader multi-case study.

La Radiologia medica
BACKGROUND: Post-processing and interpretation of coronary CT angiography (CCTA) imaging are time-consuming and dependent on the reader's experience. An automated deep learning (DL)-based imaging reconstruction and diagnosis system was developed to i...

How scan parameter choice affects deep learning-based coronary artery disease assessment from computed tomography.

Scientific reports
Recently, algorithms capable of assessing the severity of Coronary Artery Disease (CAD) in form of the Coronary Artery Disease-Reporting and Data System (CAD-RADS) grade from Coronary Computed Tomography Angiography (CCTA) scans using Deep Learning (...

A deep learning-based fully automatic and clinical-ready framework for regional myocardial segmentation and myocardial ischemia evaluation.

Medical & biological engineering & computing
Myocardial ischemia diagnosis with CT perfusion imaging (CTP) is important in coronary artery disease management. Traditional analysis procedure is time-consuming and error-prone due to the semi-manual and operator-dependent nature. To improve the di...

Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction.

JACC. Cardiovascular imaging
BACKGROUND: Epicardial adipose tissue (EAT) volume is a marker of visceral obesity that can be measured in coronary computed tomography angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been ...

Prediction of Coronary Artery Calcium Using Deep Learning of Echocardiograms.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
BACKGROUND: Coronary artery calcification (CAC), often assessed by computed tomography (CT), is a powerful marker of coronary artery disease that can guide preventive therapies. Computed tomographies, however, are not always accessible or serially ob...