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Ventricular Function, Left

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Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function.

JACC. Cardiovascular imaging Jul 17, 2019
OBJECTIVES: This study sought to develop a fully automated framework for cardiac function analysis from cardiac magnetic resonance (CMR), including comprehensive quality control (QC) algorithms to detect erroneous output.
Reproducibility of Results Aged Male Ventricular Function, Left Stroke Volume Heart Diseases Humans Image Interpretation, Computer-Assisted Female Quality Indicators, Health Care Automation Middle Aged Magnetic Resonance Imaging, Cine Diagnosis, Computer-Assisted Ventricular Function, Right Deep Learning Predictive Value of Tests Quality Control
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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.
Humans Machine Learning Decision Support Techniques Retrospective Studies Heart Transplantation Ohio Disease Progression Baltimore Time Factors Recovery of Function Heart Failure Predictive Value of Tests Ventricular Function, Left Cardiac Resynchronization Therapy Patient Selection Stroke Volume Clinical Decision-Making Echocardiography Female Progression-Free Survival Risk Factors Middle Aged Practice Guidelines as Topic Aged Heart-Assist Devices Risk Assessment Male
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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-...
Humans Clinical Decision-Making Female Aged Heart Failure Treatment Outcome Echocardiography Cardiac Resynchronization Therapy Middle Aged Support Vector Machine Ventricular Remodeling Aged, 80 and over Electrocardiography Predictive Value of Tests Image Interpretation, Computer-Assisted Male Signal Processing, Computer-Assisted Ventricular Function, Left Patient Selection
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Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China.

BMJ open May 16, 2019
INTRODUCTION: Left ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognis...
Humans Machine Learning Research Design Prospective Studies Retrospective Studies Heart Failure Death, Sudden, Cardiac Ventricular Function, Left Stroke Volume China Multicenter Studies as Topic Registries
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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 ...
Deep Learning Predictive Value of Tests Myocardial Infarction Image Interpretation, Computer-Assisted Observer Variation Retrospective Studies Female Reproducibility of Results Aged Aged, 80 and over Male Ventricular Function, Left Humans Echocardiography Middle Aged Diagnosis, Computer-Assisted
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New Multiparametric Analysis of Cardiac Dyssynchrony: Machine Learning and Prediction of Response to CRT.

JACC. Cardiovascular imaging Apr 17, 2019
Humans Image Interpretation, Computer-Assisted Machine Learning Decision Support Techniques Predictive Value of Tests Clinical Decision-Making Retrospective Studies Ventricular Function, Left Signal Processing, Computer-Assisted Echocardiography Heart Failure Electrocardiography Cardiac Resynchronization Therapy Treatment Outcome Ventricular Dysfunction, Left
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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.
Deep Learning Electrocardiography Predictive Value of Tests Signal Processing, Computer-Assisted Heart Rate Ventricular Dysfunction, Left Humans Ventricular Function, Left Female Prospective Studies Reproducibility of Results Stroke Volume Aged Prognosis Echocardiography Aged, 80 and over Middle Aged Male Diagnosis, Computer-Assisted
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Automatic quantification of the LV function and mass: A deep learning approach for cardiovascular MRI.

Computer methods and programs in biomedicine Dec 12, 2018
OBJECTIVE: This paper proposes a novel approach for automatic left ventricle (LV) quantification using convolutional neural networks (CNN).
Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans Neural Networks, Computer Algorithms Ventricular Function, Left Deep Learning
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Serum Peroxisome Proliferator-activated Receptor Gamma Coactivator-1α Related to Myocardial Energy Expenditure in Patients With Chronic Heart Failure.

The American journal of the medical sciences Dec 7, 2018
BACKGROUND: Peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α) plays key roles in controlling cardiac metabolism and function. Myocardial energy expenditure (MEE) can reflect myocardial energy metabolism and cardiac function. Wh...
Energy Metabolism Natriuretic Peptide, Brain Correlation of Data Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-alpha Humans Echocardiography Female Middle Aged Aged Myocardium Male Peptide Fragments Ventricular Function, Left Heart Failure
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Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy.

European journal of heart failure Oct 17, 2018
AIMS: We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to card...
Middle Aged Humans Follow-Up Studies Machine Learning Heart Ventricles Aged Algorithms Heart Failure Male Retrospective Studies Cardiac Resynchronization Therapy Ventricular Function, Left Female Stroke Volume Reproducibility of Results Echocardiography
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