AIMC Topic: Ventricular Dysfunction, Left

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Deep Learning for Assessment of Left Ventricular Ejection Fraction from Echocardiographic Images.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography Feb 25, 2020
Ventricular Function, Left Stroke Volume Echocardiography Deep Learning Ventricular Dysfunction, Left Humans
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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...
Cell-Free Nucleic Acids Aged Humans Natriuretic Peptide, Brain Ventricular Dysfunction, Left Cell Death Biomarkers Male Correlation of Data Prognosis Myocytes, Cardiac Heart Failure Stroke Volume Liquid Biopsy Female Ventricular Remodeling Echocardiography
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A Multicenter, Scan-Rescan, Human and Machine Learning CMR Study to Test Generalizability and Precision in Imaging Biomarker Analysis.

Circulation. Cardiovascular imaging Sep 24, 2019
BACKGROUND: Automated analysis of cardiac structure and function using machine learning (ML) has great potential, but is currently hindered by poor generalizability. Comparison is traditionally against clinicians as a reference, ignoring inherent hum...
Reproducibility of Results Male Cardiovascular Diseases Stroke Volume Middle Aged Humans Machine Learning Biomarkers Female Magnetic Resonance Imaging Ventricular Dysfunction, Left Image Interpretation, Computer-Assisted
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Flow network tracking for spatiotemporal and periodic point matching: Applied to cardiac motion analysis.

Medical image analysis Apr 18, 2019
The accurate quantification of left ventricular (LV) deformation/strain shows significant promise for quantitatively assessing cardiac function for use in diagnosis and therapy planning. However, accurate estimation of the displacement of myocardial ...
Animals Echocardiography Imaging, Three-Dimensional Image Processing, Computer-Assisted Dogs Neural Networks, Computer Algorithms Motion Ventricular Dysfunction, Left
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New Multiparametric Analysis of Cardiac Dyssynchrony: Machine Learning and Prediction of Response to CRT.

JACC. Cardiovascular imaging Apr 17, 2019
Echocardiography Decision Support Techniques Cardiac Resynchronization Therapy Electrocardiography Retrospective Studies Signal Processing, Computer-Assisted Clinical Decision-Making Humans Ventricular Dysfunction, Left Machine Learning Heart Failure Ventricular Function, Left Image Interpretation, Computer-Assisted Treatment Outcome Predictive Value of Tests
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Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction.

Journal of cardiovascular electrophysiology Mar 10, 2019
OBJECTIVES: We sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12-lead electrocardiogram (ECG) in a large prospective cohort.
Diagnosis, Computer-Assisted Deep Learning Electrocardiography Predictive Value of Tests Signal Processing, Computer-Assisted Ventricular Function, Left Stroke Volume Echocardiography Middle Aged Heart Rate Ventricular Dysfunction, Left Humans Prospective Studies Prognosis Aged Aged, 80 and over Male Female Reproducibility of Results
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Hypertonic Saline Modulates Heart Function and Myocardial Inflammatory Alterations in Brain-Dead Rats.

The Journal of surgical research Oct 23, 2018
BACKGROUND: Brain death (BD) in potential organ donors is responsible for hemodynamic instability and organ hypoperfusion, leading to myocardial dysfunction. Hypertonic saline (HS) is a volume expander with positive effects on hemodynamics and immuno...
Rats Myocardium Animals Sodium Brain Death Ventricular Dysfunction, Left Male Saline Solution, Hypertonic Hemodynamics Rats, Wistar
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Phenotypic Clustering of Left Ventricular Diastolic Function Parameters: Patterns and Prognostic Relevance.

JACC. Cardiovascular imaging Apr 18, 2018
OBJECTIVES: This study sought to explore the natural clustering of echocardiographic variables used for assessing left ventricular (LV) diastolic dysfunction (DD) in order to isolate high-risk phenotypic patterns and assess their prognostic significa...
Echocardiography, Doppler, Pulsed Aged Image Interpretation, Computer-Assisted Aged, 80 and over Cause of Death Pattern Recognition, Automated Male Heart Ventricles Diastole Ventricular Function, Left Progression-Free Survival Echocardiography, Doppler, Color Disease Progression Ventricular Dysfunction, Left Phenotype Middle Aged Cluster Analysis Humans Female Retrospective Studies Risk Assessment Predictive Value of Tests Machine Learning Risk Factors Hospitalization
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Artificial Intelligence-Based Assessment of Left Ventricular Filling Pressures From 2-Dimensional Cardiac Ultrasound Images.

JACC. Cardiovascular imaging Jul 19, 2017
Machine Learning Decision Support Techniques Prognosis Reproducibility of Results Predictive Value of Tests Heart Ventricles Ventricular Remodeling Ventricular Function, Left Echocardiography Humans Ventricular Dysfunction, Left Image Interpretation, Computer-Assisted
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A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.

Medical image analysis Feb 6, 2016
Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms...
Models, Cardiovascular Subtraction Technique Magnetic Resonance Imaging, Cine Ventricular Dysfunction, Left Image Interpretation, Computer-Assisted Pattern Recognition, Automated Image Enhancement Algorithms Computer Simulation Humans Reproducibility of Results Machine Learning Sensitivity and Specificity Heart Ventricles
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