AIMC Topic: Ventricular Function, Left

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Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study.

Journal of cardiovascular translational research Jul 17, 2024
Heart failure (HF) is defined as the inability of the heart to meet body oxygen demand requiring an elevation in left ventricular filling pressures (LVP) to compensate. LVP increase can be assessed in the cardiac catheterization laboratory, but this ...
Machine Learning Animals Ventricular Function, Left Stroke Volume Swine Phonocardiography Ventricular Pressure Heart Failure Heart Rate Oximetry Swine, Miniature Heart Sounds Electrocardiography Disease Models, Animal Predictive Value of Tests Signal Processing, Computer-Assisted Feasibility Studies
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Identifying high-risk Fontan phenotypes using K-means clustering of cardiac magnetic resonance-based dyssynchrony metrics.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance Jul 14, 2024
BACKGROUND: Individuals with a Fontan circulation encompass a heterogeneous group with adverse outcomes linked to ventricular dilation, dysfunction, and dyssynchrony. The purpose of this study was to assess if unsupervised machine learning cluster an...
Heart Transplantation Risk Assessment Fontan Procedure Young Adult Cluster Analysis Humans Adolescent Heart Defects, Congenital Adult Time Factors Unsupervised Machine Learning Phenotype Magnetic Resonance Imaging, Cine Biomechanical Phenomena Retrospective Studies Child Predictive Value of Tests Myocardial Contraction Male Female Treatment Outcome Ventricular Dysfunction, Left Ventricular Function, Left Risk Factors Image Interpretation, Computer-Assisted
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2-Dimensional Echocardiographic Global Longitudinal Strain With Artificial Intelligence Using Open Data From a UK-Wide Collaborative.

JACC. Cardiovascular imaging Jul 10, 2024
BACKGROUND: Global longitudinal strain (GLS) is reported to be more reproducible and prognostic than ejection fraction. Automated, transparent methods may increase trust and uptake.
United Kingdom Humans Myocardial Contraction Neural Networks, Computer Global Longitudinal Strain Machine Learning Reproducibility of Results Predictive Value of Tests Biomechanical Phenomena Ventricular Dysfunction, Left Ventricular Function, Left Image Interpretation, Computer-Assisted Echocardiography Datasets as Topic Consensus Observer Variation
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Multicenter validation study for automated left ventricular ejection fraction assessment using a handheld ultrasound with artificial intelligence.

Scientific reports Jul 4, 2024
We sought to validate the ability of a novel handheld ultrasound device with an artificial intelligence program (AI-POCUS) that automatically assesses left ventricular ejection fraction (LVEF). AI-POCUS was used to prospectively scan 200 patients in ...
Humans Artificial Intelligence Stroke Volume Echocardiography Middle Aged Ultrasonography Heart Ventricles Ventricular Dysfunction, Left Prospective Studies Female Aged Male Ventricular Function, Left
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Applying natural language processing to identify emergency department and observation encounters for worsening heart failure.

ESC heart failure May 13, 2024
AIMS: Worsening heart failure (WHF) events occurring in non-inpatient settings are becoming increasingly recognized, with implications for prognostication. We evaluate the performance of a natural language processing (NLP)-based approach compared wit...
Female Aged Male Ventricular Function, Left Stroke Volume Humans Prognosis Retrospective Studies Heart Failure Middle Aged Disease Progression Follow-Up Studies Natural Language Processing Emergency Service, Hospital
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Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning.

PloS one May 13, 2024
Heart failure (HF) encompasses a diverse clinical spectrum, including instances of transient HF or HF with recovered ejection fraction, alongside persistent cases. This dynamic condition exhibits a growing prevalence and entails substantial healthcar...
Humans Neural Networks, Computer Machine Learning Support Vector Machine Male Circadian Rhythm Female Ventricular Function, Left Heart Failure Aged Stroke Volume Middle Aged Electrocardiography
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DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR.

Atherosclerosis Apr 18, 2024
BACKGROUND AND AIMS: This study investigated the additional prognostic value of epicardial adipose tissue (EAT) volume for major adverse cardiovascular events (MACE) in patients undergoing stress cardiac magnetic resonance (CMR) imaging.
Magnetic Resonance Imaging Adipose Tissue Humans Predictive Value of Tests Prognosis Coronary Artery Disease Retrospective Studies Ventricular Function, Left Pericardium Myocardial Infarction Stroke Volume Female Magnetic Resonance Imaging, Cine Middle Aged Reproducibility of Results Epicardial Adipose Tissue Risk Assessment Risk Factors Deep Learning Aged Male
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Radiomics-based detection of acute myocardial infarction on noncontrast enhanced midventricular short-axis cine CMR images.

The international journal of cardiovascular imaging Apr 17, 2024
Cardiac magnetic resonance cine images are primarily used to evaluate functional consequences, whereas limited information is extracted from the noncontrast pixel-wise myocardial signal intensity pattern. In this study we want to assess whether chara...
Myocardial Infarction Image Interpretation, Computer-Assisted Magnetic Resonance Imaging, Cine Ventricular Function, Left Middle Aged Case-Control Studies Support Vector Machine Predictive Value of Tests Reproducibility of Results Aged Adult Radiomics Male Humans Retrospective Studies Female
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The Road to Robust and Automated Strain Measurements in Echocardiography by Deep Learning.

JACC. Cardiovascular imaging Apr 10, 2024
Automation Myocardial Contraction Humans Reproducibility of Results Ventricular Function, Left Echocardiography Deep Learning Predictive Value of Tests Image Interpretation, Computer-Assisted
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Diastolic function assessment with four-dimensional flow cardiovascular magnetic resonance using automatic deep learning E/A ratio analysis.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance Mar 30, 2024
BACKGROUND: Diastolic left ventricular (LV) dysfunction is a powerful contributor to the symptoms and prognosis of patients with heart failure. In patients with depressed LV systolic function, the E/A ratio, the ratio between the peak early (E) and t...
Image Interpretation, Computer-Assisted Automation Myocardial Ischemia Humans Echocardiography, Doppler Female Blood Flow Velocity Diastole Deep Learning Magnetic Resonance Imaging, Cine Chronic Disease Reproducibility of Results Predictive Value of Tests Aged Mitral Valve Male Ventricular Dysfunction, Left Ventricular Function, Left Middle Aged
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