AIMC Topic: Echocardiography

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Hierarchical Class Incremental Learning of Anatomical Structures in Fetal Echocardiography Videos.

IEEE journal of biomedical and health informatics Feb 12, 2020
This paper proposes an ultrasound video interpretation algorithm that enables novel classes or instances to be added over time, without significantly compromising prediction abilities on prior representations. The motivating application is diagnostic...
Humans Algorithms Ultrasonography, Prenatal Deep Learning Image Interpretation, Computer-Assisted Fetal Heart Pregnancy Female Echocardiography
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Deep Atlas Network for Efficient 3D Left Ventricle Segmentation on Echocardiography.

Medical image analysis Jan 13, 2020
We proposed a novel efficient method for 3D left ventricle (LV) segmentation on echocardiography, which is important for cardiac disease diagnosis. The proposed method effectively overcame the 3D echocardiography's challenges: high dimensional data, ...
Deep Learning Imaging, Three-Dimensional Heart Ventricles Humans Echocardiography
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Machine Learning in Fetal Cardiology: What to Expect.

Fetal diagnosis and therapy Jan 7, 2020
In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approac...
Prenatal Diagnosis Fetal Heart Echocardiography Ultrasonography, Prenatal Heart Defects, Congenital Pregnancy Humans Female Machine Learning
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DW-Net: A cascaded convolutional neural network for apical four-chamber view segmentation in fetal echocardiography.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Dec 23, 2019
Fetal echocardiography (FE) is a widely used medical examination for early diagnosis of congenital heart disease (CHD). The apical four-chamber view (A4C) is an important view among early FE images. Accurate segmentation of crucial anatomical structu...
Artifacts Heart Defects, Congenital Fetal Heart Female Echocardiography Ultrasonography, Prenatal Neural Networks, Computer Humans Pregnancy
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Enhanced Diagnosis of Severe Aortic Stenosis Using Artificial Intelligence: A Proof-of-Concept Study of 530,871 Echocardiograms.

JACC. Cardiovascular imaging Dec 18, 2019
Echocardiography Middle Aged Severity of Illness Index Hemodynamics Adult Male Ventricular Function, Left Predictive Value of Tests Image Interpretation, Computer-Assisted Proof of Concept Study Diagnosis, Computer-Assisted Aortic Valve Stenosis Aortic Valve Humans Artificial Intelligence Databases, Factual Aged Aged, 80 and over Prognosis Female
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On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra- Observer Variability in 2D Echocardiography Quality Assessment.

IEEE transactions on medical imaging Dec 13, 2019
Uncertainty of labels in clinical data resulting from intra-observer variability can have direct impact on the reliability of assessments made by deep neural networks. In this paper, we propose a method for modelling such uncertainty in the context o...
Humans Observer Variation Neural Networks, Computer Uncertainty Reproducibility of Results Echocardiography
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MFP-Unet: A novel deep learning based approach for left ventricle segmentation in echocardiography.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Oct 28, 2019
Segmentation of the Left ventricle (LV) is a crucial step for quantitative measurements such as area, volume, and ejection fraction. However, the automatic LV segmentation in 2D echocardiographic images is a challenging task due to ill-defined border...
Echocardiography Deep Learning Image Processing, Computer-Assisted Heart Ventricles
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ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial.

American heart journal Oct 25, 2019
BACKGROUND: A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed and validated. The algorithm was incorporated into the electronic health record (EHR) to automaticall...
Cost-Benefit Analysis Cardiac Output, Low Asymptomatic Diseases Humans Electrocardiography Electronic Health Records Artificial Intelligence Sample Size Echocardiography Informed Consent Prospective Studies Deep Learning Heart Failure
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Clinical Significance of Circulating Cardiomyocyte-Specific Cell-Free DNA in Patients With Heart Failure: A Proof-of-Concept Study.

The Canadian journal of cardiology Oct 19, 2019
We investigated clinical significance of cell-free DNA (cfDNA) in heart failure. This study enrolled 32 heart failure patients and 28 control subjects. Total cfDNA levels were not different between groups (P = 0.343). Bisulfite-digital polymerase cha...
Liquid Biopsy Myocytes, Cardiac Natriuretic Peptide, Brain Stroke Volume Correlation of Data Female Ventricular Remodeling Echocardiography Humans Aged Cell Death Ventricular Dysfunction, Left Cell-Free Nucleic Acids Biomarkers Male Heart Failure Prognosis
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A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type.

Scandinavian cardiovascular journal : SCJ Oct 18, 2019
In heart failure, invasive angiography is often employed to differentiate ischaemic from non-ischaemic cardiomyopathy. We aim to examine the predictive value of echocardiographic strain features alone and in combination with other features to differ...
Humans Neural Networks, Computer Prognosis Reproducibility of Results Predictive Value of Tests Aged Heart Failure Retrospective Studies Image Interpretation, Computer-Assisted Female Echocardiography Cardiomyopathies Male Diagnosis, Computer-Assisted Middle Aged Ventricular Function, Left Diagnosis, Differential Stroke Volume
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