AIMC Topic: Echocardiography

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Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning.

IEEE transactions on medical imaging Oct 7, 2019
Fetal congenital heart disease (FHD) is a common and serious congenital malformation in children. In Asia, FHD birth defect rates have reached as high as 9.3%. For the early detection of birth defects and mortality, echocardiography remains the most ...
Humans Ultrasonography, Prenatal Deep Learning Video Recording Image Interpretation, Computer-Assisted Heart Defects, Congenital Algorithms Pregnancy Female Echocardiography
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Echocardiographic Data in Artificial Intelligence Research: Primer on Concepts of Big Data and Latent States.

JACC. Cardiovascular imaging Sep 18, 2019
Humans Artificial Intelligence Echocardiography Big Data Predictive Value of Tests Data Mining
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Combining Artificial Intelligence With Human Insight to Automate Echocardiography.

Circulation. Cardiovascular imaging Sep 16, 2019
Ventricular Function, Left Stroke Volume Echocardiography Humans Artificial Intelligence Machine Learning Algorithms
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Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert.

Circulation. Cardiovascular imaging Sep 16, 2019
BACKGROUND: Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by model-based calculation of end-systolic and end-diastolic LV volume...
Stroke Volume Humans Echocardiography Machine Learning Automation Retrospective Studies Aged Female Sensitivity and Specificity Reproducibility of Results Male Ventricular Function, Left
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PV-LVNet: Direct left ventricle multitype indices estimation from 2D echocardiograms of paired apical views with deep neural networks.

Medical image analysis Sep 10, 2019
Accurate direct estimation of the left ventricle (LV) multitype indices from two-dimensional (2D) echocardiograms of paired apical views, i.e., paired apical four-chamber (A4C) and two-chamber (A2C), is of great significance to clinically evaluate ca...
Heart Ventricles Image Enhancement Humans Neural Networks, Computer Echocardiography
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Denoising and artefact removal for transthoracic echocardiographic imaging in congenital heart disease: utility of diagnosis specific deep learning algorithms.

The international journal of cardiovascular imaging Jul 19, 2019
Deep learning (DL) algorithms are increasingly used in cardiac imaging. We aimed to investigate the utility of DL algorithms in de-noising transthoracic echocardiographic images and removing acoustic shadowing artefacts specifically in patients with ...
Predictive Value of Tests Image Interpretation, Computer-Assisted Artifacts Signal-To-Noise Ratio Echocardiography Middle Aged Case-Control Studies Deep Learning Heart Defects, Congenital Humans Female Reproducibility of Results Young Adult Adult Male
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Utilization of Artificial Intelligence in Echocardiography.

Circulation journal : official journal of the Japanese Circulation Society Jun 29, 2019
Echocardiography has a central role in the diagnosis and management of cardiovascular disease. Precise and reliable echocardiographic assessment is required for clinical decision-making. Even if the development of new technologies (3-dimentional echo...
Echocardiography Diagnosis, Computer-Assisted Deep Learning Predictive Value of Tests Workflow Diffusion of Innovation Image Interpretation, Computer-Assisted Machine Learning Forecasting Reproducibility of Results Cardiovascular Diseases Humans
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Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines.

Circulation. Arrhythmia and electrophysiology Jun 20, 2019
BACKGROUND: Cardiac resynchronization therapy (CRT) has significant nonresponse rates. We assessed whether machine learning (ML) could predict CRT response beyond current guidelines.
Ohio Baltimore Progression-Free Survival Practice Guidelines as Topic Humans Ventricular Function, Left Heart-Assist Devices Stroke Volume Heart Transplantation Echocardiography Predictive Value of Tests Middle Aged Patient Selection Female Risk Assessment Heart Failure Machine Learning Risk Factors Disease Progression Cardiac Resynchronization Therapy Decision Support Techniques Aged Time Factors Retrospective Studies Male Recovery of Function Clinical Decision-Making
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Ventricular geometry-regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography.

The international journal of cardiovascular imaging May 18, 2019
Up to one-third of patients selected by current guidelines do not respond to cardiac resynchronization therapy (CRT), the aim of this study was to find out novel analytical approaches to improve pre-implantation CRT response prediction. Among 31 pre-...
Cardiac Resynchronization Therapy Middle Aged Aged, 80 and over Ventricular Remodeling Electrocardiography Male Humans Image Interpretation, Computer-Assisted Ventricular Function, Left Treatment Outcome Predictive Value of Tests Clinical Decision-Making Echocardiography Support Vector Machine Female Signal Processing, Computer-Assisted Aged Patient Selection Heart Failure
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A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images.

JACC. Cardiovascular imaging May 15, 2019
OBJECTIVES: This study investigated whether a deep convolutional neural network (DCNN) could provide improved detection of regional wall motion abnormalities (RWMAs) and differentiate among groups of coronary infarction territories from conventional ...
Myocardial Infarction Image Interpretation, Computer-Assisted Observer Variation Retrospective Studies Female Middle Aged Reproducibility of Results Aged, 80 and over Humans Diagnosis, Computer-Assisted Aged Male Deep Learning Ventricular Function, Left Predictive Value of Tests Echocardiography
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