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Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection Fraction.

International journal of clinical practice
INTRODUCTION: Point-of-care ultrasound (POCUS) use is now universal among nonexperts. Artificial intelligence (AI) is currently employed by nonexperts in various imaging modalities to assist in diagnosis and decision making.

Heart Failure Assessment Using Multiparameter Polar Representations and Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Heart failure refers to the inability of the heart to pump enough amount of blood to the body. Nearly 7 million people die every year because of its complications. Current gold-standard screening techniques through echocardiography do not incorporate...

Deep Learning-Enabled Assessment of Left Heart Structure and Function Predicts Cardiovascular Outcomes.

Journal of the American College of Cardiology
BACKGROUND: Deep learning interpretation of echocardiographic images may facilitate automated assessment of cardiac structure and function.

High-resolution spiral real-time cardiac cine imaging with deep learning-based rapid image reconstruction and quantification.

NMR in biomedicine
The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) and deep learning (DL)-based segmentation approach to quantify the left ventricular ejection fraction (LVEF) for high-reso...

The PACIFIC ontology for heterogeneous data management in cardiology.

Journal of biomedical informatics
With the emergence of health data warehouses and major initiatives to collect and analyze multi-modal and multisource data, data organization becomes central. In the PACIFIC-PRESERVED (PhenomApping, ClassIFication, and Innovation for Cardiac Dysfunct...

An attention-based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility and accuracy.

Echocardiography (Mount Kisco, N.Y.)
AIM: To test the feasibility and accuracy of a new attention-based deep learning (DL) method for right ventricular (RV) quantification using 2D echocardiography (2DE) with cardiac magnetic resonance imaging (CMR) as reference.

Pragmatic Evaluation of a Deep-Learning Algorithm to Automate Ejection Fraction on Hand-Held, Point-of-Care Echocardiography in a Cardiac Surgical Operating Room.

Journal of cardiothoracic and vascular anesthesia
OBJECTIVE: To test the correlation of ejection fraction (EF) estimated by a deep-learning-based, automated algorithm (Auto EF) versus an EF estimated by Simpson's method.

Assessment of deep learning segmentation for real-time free-breathing cardiac magnetic resonance imaging at rest and under exercise stress.

Scientific reports
In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed...

Characterizing advanced heart failure risk and hemodynamic phenotypes using interpretable machine learning.

American heart journal
BACKGROUND: Although previous risk models exist for advanced heart failure with reduced ejection fraction (HFrEF), few integrate invasive hemodynamics or support missing data. This study developed and validated a heart failure (HF) hemodynamic risk a...

Myocardial scar and left ventricular ejection fraction classification for electrocardiography image using multi-task deep learning.

Scientific reports
Myocardial scar (MS) and left ventricular ejection fraction (LVEF) are vital cardiovascular parameters, conventionally determined using cardiac magnetic resonance (CMR). However, given the high cost and limited availability of CMR in resource-constra...