AIMC Topic: Stroke Volume

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An Automatic Approach Using ELM Classifier for HFpEF Identification Based on Heart Sound Characteristics.

Journal of medical systems Jul 15, 2019
Heart failure with preserved ejection fraction (HFpEF) is a complex and heterogeneous clinical syndrome. For the purpose of assisting HFpEF diagnosis, a non-invasive method using extreme learning machine and heart sound (HS) characteristics was provi...
Stroke Volume Image Processing, Computer-Assisted Logistic Models Heart Failure Markov Chains Heart Sounds Humans Machine Learning Algorithms
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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.
Cardiac Resynchronization Therapy Progression-Free Survival Practice Guidelines as Topic Heart-Assist Devices Heart Transplantation Time Factors Recovery of Function Predictive Value of Tests Patient Selection Heart Failure Clinical Decision-Making Ohio Female Humans Middle Aged Baltimore Ventricular Function, Left Risk Factors Machine Learning Risk Assessment Aged Decision Support Techniques Disease Progression Male Stroke Volume Retrospective Studies Echocardiography
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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 Ventricular Function, Left Death, Sudden, Cardiac Stroke Volume China Multicenter Studies as Topic Registries Heart Failure
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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.
Stroke Volume Echocardiography Middle Aged Diagnosis, Computer-Assisted Deep Learning Reproducibility of Results Aged Aged, 80 and over Male Ventricular Function, Left Electrocardiography Predictive Value of Tests Signal Processing, Computer-Assisted Heart Rate Ventricular Dysfunction, Left Humans Prospective Studies Prognosis Female
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Contribution of Cardiovascular Reserve to Prognostic Categories of Heart Failure With Preserved Ejection Fraction: A Classification Based on Machine Learning.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography Feb 1, 2019
BACKGROUND: The authors used cluster analysis of data from cardiovascular domains associated with exercise intolerance to help define prognostic phenotypes of patients with heart failure with preserved ejection fraction (HFpEF).
Middle Aged Longitudinal Studies Exercise Test Heart Failure Exercise Tolerance Female Heart Function Tests Aged Humans Machine Learning Male Phenotype Stroke Volume Prospective Studies Echocardiography Biomarkers Prognosis
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Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram.

Nature medicine Jan 7, 2019
Asymptomatic left ventricular dysfunction (ALVD) is present in 3-6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found. An inexpensive, noninvasive screening tool for ALVD in the doctor's ...
Adult Sensitivity and Specificity Male Stroke Volume Middle Aged Neural Networks, Computer Heart ROC Curve Artificial Intelligence Myocardial Contraction Humans Electrocardiography Female Mass Screening Aged Aged, 80 and over
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Semiautomatic Three-Dimensional Threshold-Based Cardiac Computed Tomography Ventricular Volumetry in Repaired Tetralogy of Fallot: Comparison with Cardiac Magnetic Resonance Imaging.

Korean journal of radiology Dec 27, 2018
OBJECTIVE: To assess the accuracy and potential bias of computed tomography (CT) ventricular volumetry using semiautomatic three-dimensional (3D) threshold-based segmentation in repaired tetralogy of Fallot, and to compare them to those of two-dimens...
Magnetic Resonance Imaging Male Humans Cardiac Surgical Procedures Stroke Volume Retrospective Studies Heart Ventricles Adolescent Female Dimensional Measurement Accuracy Child Young Adult Tetralogy of Fallot Cone-Beam Computed Tomography Adult
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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...
Algorithms Retrospective Studies Female Reproducibility of Results Aged Male Ventricular Function, Left Stroke Volume Echocardiography Middle Aged Humans Machine Learning Follow-Up Studies Heart Ventricles Heart Failure Cardiac Resynchronization Therapy
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Automated cardiovascular magnetic resonance image analysis with fully convolutional networks.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance Sep 14, 2018
BACKGROUND: Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction ...
Aged Magnetic Resonance Imaging, Cine Male Ventricular Function, Right Ventricular Function, Left Myocardial Contraction Stroke Volume Humans Middle Aged Predictive Value of Tests Deep Learning Neural Networks, Computer Heart Diseases Databases, Factual Image Interpretation, Computer-Assisted Female Observer Variation Reproducibility of Results Automation
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Diagnosis of Heart Failure With Preserved Ejection Fraction: Machine Learning of Spatiotemporal Variations in Left Ventricular Deformation.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography Aug 23, 2018
BACKGROUND: Stress testing helps diagnose heart failure with preserved ejection fraction (HFpEF), but there are no established criteria for quantifying left ventricular (LV) functional reserve. The aim of this study was to investigate whether compreh...
Exercise Test Heart Ventricles Heart Failure Exercise Tolerance Myocardial Contraction Male Humans Ventricular Function, Left Machine Learning Stroke Volume Prospective Studies Echocardiography Female Follow-Up Studies Aged
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