AIMC Topic: Echocardiography
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EchoEFNet: Multi-task deep learning network for automatic calculation of left ventricular ejection fraction in 2D echocardiography.
Computers in biology and medicine
Feb 24, 2023
Left ventricular ejection fraction (LVEF) is essential for evaluating left ventricular systolic function. However, its clinical calculation requires the physician to interactively segment the left ventricle and obtain the mitral annulus and apical la...
Video-Based Deep Learning for Automated Assessment of Left Ventricular Ejection Fraction in Pediatric Patients.
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
Feb 7, 2023
BACKGROUND: Significant interobserver and interstudy variability occurs for left ventricular (LV) functional indices despite standardization of measurement techniques. Artificial intelligence models trained on adult echocardiograms are not likely to ...
Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation.
Sensors (Basel, Switzerland)
Jan 28, 2023
The segmentation of the left ventricle endocardium (LV) and the left ventricle epicardium (LV) in echocardiography plays an important role in clinical diagnosis. Recently, deep neural networks have been the most commonly used approach for echocardiog...
Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart.
Computational and mathematical methods in medicine
Jan 24, 2023
Congenital heart defect (CHD) refers to the overall structural abnormality of the heart or large blood vessels in the chest cavity. It is the most common type of fetal congenital defects. Prenatal diagnosis of congenital heart disease can improve the...
Automated Detection of Aortic Stenosis Using Machine Learning.
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
Jan 11, 2023
BACKGROUND: Aortic stenosis (AS) is a degenerative valve condition that is underdiagnosed and undertreated. Detection of AS using limited two-dimensional echocardiography could enable screening and improve appropriate referral and treatment of this c...
A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection.
Scientific reports
Jan 2, 2023
Echocardiography is the first-line diagnostic technique for heart diseases. Although artificial intelligence techniques have made great improvements in the analysis of echocardiography, the major limitations remain to be the built neural networks are...
Prediction of Coronary Artery Calcium Using Deep Learning of Echocardiograms.
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
Dec 23, 2022
BACKGROUND: Coronary artery calcification (CAC), often assessed by computed tomography (CT), is a powerful marker of coronary artery disease that can guide preventive therapies. Computed tomographies, however, are not always accessible or serially ob...
Quantifying Valve Regurgitation Using 3-D Doppler Ultrasound Images and Deep Learning.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Nov 24, 2022
Accurate quantification of cardiac valve regurgitation jets is fundamental for guiding treatment. Cardiac ultrasound is the preferred diagnostic tool, but current methods for measuring the regurgitant volume (RVol) are limited by low accuracy and hig...
Semantic segmentation method for myocardial contrast echocardiogram based on DeepLabV3+ deep learning architecture.
Mathematical biosciences and engineering : MBE
Nov 14, 2022
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
Nov 9, 2022
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...
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