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Heart

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Deep Learning Model for Coronary Angiography.

Journal of cardiovascular translational research
The visual inspection of coronary artery stenosis is known to be significantly affected by variation, due to the presence of other tissues, camera movements, and uneven illumination. More accurate and intelligent coronary angiography diagnostic model...

Correlation analysis of deep learning methods in S-ICD screening.

Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc
BACKGROUND: Machine learning methods are used in the classification of various cardiovascular diseases through ECG data analysis. The concept of varying subcutaneous implantable cardiac defibrillator (S-ICD) eligibility, owing to the dynamicity of EC...

Artificial Muscles and Soft Robotic Devices for Treatment of End-Stage Heart Failure.

Advanced materials (Deerfield Beach, Fla.)
Medical soft robotics constitutes a rapidly developing field in the treatment of cardiovascular diseases, with a promising future for millions of patients suffering from heart failure worldwide. Herein, the present state and future direction of artif...

Validation of a Fully Automated Hybrid Deep Learning Cardiac Substructure Segmentation Tool for Contouring and Dose Evaluation in Lung Cancer Radiotherapy.

Clinical oncology (Royal College of Radiologists (Great Britain))
BACKGROUND AND PURPOSE: Accurate and consistent delineation of cardiac substructures is challenging. The aim of this work was to validate a novel segmentation tool for automatic delineation of cardiac structures and subsequent dose evaluation, with p...

An automated segmentation of coronary artery calcification using deep learning in specific region limitation.

Medical & biological engineering & computing
Coronary artery calcification (CAC) is a frequent disease of the arteries that supply the surface of the heart muscle. Leaving a severe disease untreated can make it permanent. Computer tomography (CT), which is well known for its ability to quantify...

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 Image Registration in Dynamic Myocardial Perfusion CT Imaging.

IEEE transactions on medical imaging
Registration of dynamic CT image sequences is a crucial preprocessing step for clinical evaluation of multiple physiological determinants in the heart such as global and regional myocardial perfusion. In this work, we present a deformable deep learni...

Automatic reorientation by deep learning to generate short-axis SPECT myocardial perfusion images.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: Single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) can be displayed both in traditional short-axis (SA) cardiac planes and polar maps for interpretation and quantification. It is essential to reorient the...

A U-Shaped Network Based on Multi-level Feature and Dual-Attention Coordination Mechanism for Coronary Artery Segmentation of CCTA Images.

Cardiovascular engineering and technology
PURPOSE: Computed tomography coronary angiography (CCTA) images provide optimal visualization of coronary arteries to aid in diagnosing coronary heart disease (CHD). With the deep convolutional neural network, this work aims to develop an intelligent...