AIMC Topic: Echocardiography

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Semantic segmentation method for myocardial contrast echocardiogram based on DeepLabV3+ deep learning architecture.

Mathematical biosciences and engineering : MBE
Myocardial contrast echocardiography (MCE) has been proposed as a method to assess myocardial perfusion for the detection of coronary artery diseases in a non-invasive way. As a critical step of automatic MCE perfusion quantification, myocardium segm...

A formal validation of a deep learning-based automated workflow for the interpretation of the echocardiogram.

Nature communications
This study compares a deep learning interpretation of 23 echocardiographic parameters-including cardiac volumes, ejection fraction, and Doppler measurements-with three repeated measurements by core lab sonographers. The primary outcome metric, the in...

Artificial intelligence for automated evaluation of aortic measurements in 2D echocardiography: Feasibility, accuracy, and reproducibility.

Echocardiography (Mount Kisco, N.Y.)
AIMS: This study sought to examine the feasibility, accuracy and reproducibility of a novel, fully automated 2D transthoracic echocardiography (2D TTE) parasternal long axis (PLAX) view aortic measurements quantification software compared to board-ce...

Machine Learning-Enabled Fully Automated Assessment of Left Ventricular Volume, Ejection Fraction and Strain: Experience in Pediatric and Young Adult Echocardiography.

Pediatric cardiology
BACKGROUND: Left ventricular (LV) volumes, ejection fraction (EF), and myocardial strain have been shown to be predictive of clinical and subclinical heart disease. Automation of LV functional assessment overcomes difficult technical challenges and c...

A Hybrid Catheter Localisation Framework in Echocardiography Based on Electromagnetic Tracking and Deep Learning Segmentation.

Computational intelligence and neuroscience
Interventional cardiology procedure is an important type of minimally invasive surgery that deals with the catheter-based treatment of cardiovascular diseases, such as coronary artery diseases, strokes, peripheral arterial diseases, and aortic diseas...

Echocardiography Segmentation With Enforced Temporal Consistency.

IEEE transactions on medical imaging
Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer variability on end-diastole and end-systole images has been reached, ...

IFT-Net: Interactive Fusion Transformer Network for Quantitative Analysis of Pediatric Echocardiography.

Medical image analysis
The task of automatic segmentation and measurement of key anatomical structures in echocardiography is critical for subsequent extraction of clinical parameters. However, the influence of boundary blur, speckle noise, and other factors increase the d...

An Automated View Classification Model for Pediatric Echocardiography Using Artificial Intelligence.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
BACKGROUND: View classification is a key step toward building a fully automated system for interpretation of echocardiograms. However, compared with adult echocardiograms, creating a view classification model for pediatric echocardiograms poses addit...