AIMC Topic: Stroke Volume

Clear Filters Showing 41 to 50 of 218 articles

Clinical utility of a rapid two-dimensional balanced steady-state free precession sequence with deep learning reconstruction.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Cardiovascular magnetic resonance (CMR) cine imaging is still limited by long acquisition times. This study evaluated the clinical utility of an accelerated two-dimensional (2D) cine sequence with deep learning reconstruction (Sonic DL) t...

Artificial intelligence-derived left ventricular strain in echocardiography in patients treated with chemotherapy.

The international journal of cardiovascular imaging
Global longitudinal strain (GLS) is an echocardiographic measure to detect chemotherapy-related cardiovascular dysfunction. However, its limited availability and the needed expertise may restrict its generalization. Artificial intelligence (AI)-based...

Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study.

Journal of cardiovascular translational research
Heart failure (HF) is defined as the inability of the heart to meet body oxygen demand requiring an elevation in left ventricular filling pressures (LVP) to compensate. LVP increase can be assessed in the cardiac catheterization laboratory, but this ...

Multicenter validation study for automated left ventricular ejection fraction assessment using a handheld ultrasound with artificial intelligence.

Scientific reports
We sought to validate the ability of a novel handheld ultrasound device with an artificial intelligence program (AI-POCUS) that automatically assesses left ventricular ejection fraction (LVEF). AI-POCUS was used to prospectively scan 200 patients in ...

Point-of-care AI-enhanced novice echocardiography for screening heart failure (PANES-HF).

Scientific reports
The increasing prevalence of heart failure (HF) in ageing populations drives demand for echocardiography (echo). There is a worldwide shortage of trained sonographers and long waiting times for expert echo. We hypothesised that artificial intelligenc...

Tricuspid valve flow measurement using a deep learning framework for automated valve-tracking 2D phase contrast.

Magnetic resonance in medicine
PURPOSE: Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow evaluation, but they are vitally important to diastolic function evaluation. We developed an automa...

Fully Automatic Quantitative Measurement of Equilibrium Radionuclide Angiocardiography Using a Convolutional Neural Network.

Clinical nuclear medicine
PURPOSE: The aim of this study was to generate deep learning-based regions of interest (ROIs) from equilibrium radionuclide angiography datasets for left ventricular ejection fraction (LVEF) measurement.

Deep learning of left atrial structure and function provides link to atrial fibrillation risk.

Nature communications
Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to asse...

Cardiac function in a large animal model of myocardial infarction at 7 T: deep learning based automatic segmentation increases reproducibility.

Scientific reports
Cardiac magnetic resonance (CMR) imaging allows precise non-invasive quantification of cardiac function. It requires reliable image segmentation for myocardial tissue. Clinically used software usually offers automatic approaches for this step. These ...