AIMC Topic: Echocardiography

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Harnessing Machine Intelligence in Automatic Echocardiogram Analysis: Current Status, Limitations, and Future Directions.

IEEE reviews in biomedical engineering
Echocardiography (echo) is a critical tool in diagnosing various cardiovascular diseases. Despite its diagnostic and prognostic value, interpretation and analysis of echo images are still widely performed manually by echocardiographers. A plethora of...

Machine learning methods to improve bedside fluid responsiveness prediction in severe sepsis or septic shock: an observational study.

British journal of anaesthesia
BACKGROUND: Passive leg raising (PLR) predicts fluid responsiveness in critical illness, although restrictions in mobilising patients often preclude this haemodynamic challenge being used. We investigated whether machine learning applied on transthor...

Artificial Intelligence-Powered Measurement of Left Ventricular Ejection Fraction Using a Handheld Ultrasound Device.

Ultrasound in medicine & biology
The aim of this study was to assess the accuracy of an algorithm for automated measurement of left ventricular ejection fraction (LVEF) available on handheld ultrasound devices (HUDs). One hundred twelve patients admitted to the cardiology department...

Echocardiography in patients with heart failure: recent advances and future perspectives.

Kardiologia polska
Echocardiography is a relatively inexpensive and widely available technique that has a pivotal role in the assessment and management of patients with heart failure (HF). Advancements in cardiac ultrasound, especially the advent of myocardial deformat...

Comparative analysis of active contour and convolutional neural network in rapid left-ventricle volume quantification using echocardiographic imaging.

Computer methods and programs in biomedicine
In cardiology, ultrasound is often used to diagnose heart disease associated with myocardial infarction. This study aims to develop robust segmentation techniques for segmenting the left ventricle (LV) in ultrasound images to check myocardium movemen...

LU-Net: A Multistage Attention Network to Improve the Robustness of Segmentation of Left Ventricular Structures in 2-D Echocardiography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semiautomatically in clinical routine and is, thus, prone to interobserver and intraobserver variabilities. R...

Real-Time Automatic Ejection Fraction and Foreshortening Detection Using Deep Learning.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Volume and ejection fraction (EF) measurements of the left ventricle (LV) in 2-D echocardiography are associated with a high uncertainty not only due to interobserver variability of the manual measurement, but also due to ultrasound acquisition error...

Impact of chronic intermittent hypoxia on the long non-coding RNA and mRNA expression profiles in myocardial infarction.

Journal of cellular and molecular medicine
Chronic intermittent hypoxia (CIH) is the primary feature of obstructive sleep apnoea (OSA), a crucial risk factor for cardiovascular diseases. Long non-coding RNAs (lncRNAs) in myocardial infarction (MI) pathogenesis have drawn considerable attentio...

Automated estimation of echocardiogram image quality in hospitalized patients.

The international journal of cardiovascular imaging
We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient grou...