AIMC Topic: Heart Ventricles

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A Comparison of Three-Dimensional Speckle Tracking Echocardiography Parameters in Predicting Left Ventricular Remodeling.

Journal of healthcare engineering Jul 30, 2020
Three-dimensional speckle tracking echocardiography (3D STE) is an emerging noninvasive method for predicting left ventricular remodeling (LVR) after acute myocardial infarction (AMI). Previous studies analyzed the predictive value of 3D STE with tra...
Coronary Angiography Percutaneous Coronary Intervention Myocardial Infarction Heart Ventricles Ventricular Remodeling Reproducibility of Results ROC Curve Adult Ventricular Dysfunction, Left Imaging, Three-Dimensional Models, Statistical Risk Factors Sensitivity and Specificity Humans Echocardiography Predictive Value of Tests Aged Male Machine Learning Middle Aged Area Under Curve Female
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Left ventricle quantification with sample-level confidence estimation via Bayesian neural network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jul 26, 2020
Quantification of cardiac left ventricle has become a hot topic due to its great significance in clinical practice. Many efforts have been devoted to LV quantification and obtained promising performance with the help of various deep neural networks w...
Heart Ventricles Uncertainty Humans Neural Networks, Computer Bayes Theorem
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Improved myocardial perfusion PET imaging using artificial neural networks.

Physics in medicine and biology Jul 20, 2020
Myocardial perfusion (MP) PET imaging plays a key role in risk assessment and stratification of patients with coronary artery disease. In this work, we proposed a patch-based artificial neural network (ANN) fusion approach that integrates information...
Humans Neural Networks, Computer Female Heart Ventricles Positron-Emission Tomography Phantoms, Imaging Myocardial Perfusion Imaging Male ROC Curve Image Processing, Computer-Assisted
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Fully automated quantification of left ventricular volumes and function in cardiac MRI: clinical evaluation of a deep learning-based algorithm.

The international journal of cardiovascular imaging Jul 16, 2020
To investigate the performance of a deep learning-based algorithm for fully automated quantification of left ventricular (LV) volumes and function in cardiac MRI. We retrospectively analysed MR examinations of 50 patients (74% men, median age 57 year...
Humans Retrospective Studies Aged, 80 and over Young Adult Adult Male Female Reproducibility of Results Aged Heart Ventricles Heart Diseases Image Interpretation, Computer-Assisted Deep Learning Automation Adolescent Magnetic Resonance Imaging, Cine Predictive Value of Tests Feasibility Studies Ventricular Function, Left Middle Aged Diagnosis, Computer-Assisted
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Non-ischemic endocardial scar geometric remodeling toward topological machine learning.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine Jul 10, 2020
Scar tissues have been important factors in determining the progression of myocardial diseases and the development of adverse cardiac failure outcomes. Accurate segmentation of the scar tissues can be helpful to the clinicians for risk prediction and...
Humans Heart Ventricles Neural Networks, Computer Machine Learning Image Processing, Computer-Assisted Cicatrix
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Automatic left ventricle segmentation in short-axis MRI using deep convolutional neural networks and central-line guided level set approach.

Computers in biology and medicine Jun 23, 2020
In the clinical diagnosis of cardiovascular diseases, left ventricle (LV) segmentation in cardiac magnetic resonance images (MRI) is an indispensable procedure for doctors. To reduce the time needed for diagnosis, we develop an automatic LV segmentat...
Neural Networks, Computer Algorithms Pericardium Image Processing, Computer-Assisted Magnetic Resonance Imaging Heart Ventricles
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Localization of origins of premature ventricular contraction in the whole ventricle based on machine learning and automatic beat recognition from 12-lead ECG.

Physiological measurement Jun 10, 2020
OBJECTIVE: The localization of origins of premature ventricular contraction (PVC) is the key factor for the success of ablation of ventricular arrhythmias. Existing methods rely heavily on manual extraction of PVC beats, which limits their applicatio...
Machine Learning Electrocardiography Signal Processing, Computer-Assisted Automation Ventricular Premature Complexes Heart Ventricles Heart Rate Humans
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Deep Learning for Improved Risk Prediction in Surgical Outcomes.

Scientific reports Jun 9, 2020
The Norwood surgical procedure restores functional systemic circulation in neonatal patients with single ventricle congenital heart defects, but this complex procedure carries a high mortality rate. In this study we address the need to provide an acc...
Hypoplastic Left Heart Syndrome Norwood Procedures Risk Monte Carlo Method Decision Making, Organizational Infant Deep Learning Length of Stay Heart Ventricles Markov Chains Humans Hospital Mortality Neural Networks, Computer Models, Statistical Infant, Newborn
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Automated detection of left ventricle in arterial input function images for inline perfusion mapping using deep learning: A study of 15,000 patients.

Magnetic resonance in medicine May 7, 2020
PURPOSE: Quantification of myocardial perfusion has the potential to improve the detection of regional and global flow reduction. Significant effort has been made to automate the workflow, where one essential step is the arterial input function (AIF)...
Algorithms Deep Learning Magnetic Resonance Imaging Heart Ventricles Perfusion Humans
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Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

PloS one May 6, 2020
OBJECTIVES: To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation.
Heart Atria Coronary Vessels Female Computed Tomography Angiography Aged Deep Learning Male Humans Heart Middle Aged Heart Ventricles
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