AIMC Topic: Tachycardia, Ventricular
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Evaluation of a deep learning-enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias.
The Journal of physiology
Apr 24, 2023
Personalized, image-based computational heart modelling is a powerful technology that can be used to improve patient-specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state-of-the-art methods s...
Development of a Visualization Deep Learning Model for Classifying Origins of Ventricular Arrhythmias.
Circulation journal : official journal of the Japanese Circulation Society
Apr 7, 2022
BACKGROUND: Several algorithms have been proposed for differentiating the right and left outflow tracts (RVOT/LVOT) arrhythmia origins from 12-lead electrocardiograms (ECGs); however, the procedure is complicated. A deep learning (DL) model, a form o...
Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death.
Circulation research
Nov 10, 2020
RATIONALE: Susceptibility to VT/VF (ventricular tachycardia/fibrillation) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside.
Humans
Neural Networks, Computer
Phenotype
Aged
Aged, 80 and over
Male
Middle Aged
Prospective Studies
Prognosis
Female
Risk Factors
Myocardial Infarction
Tachycardia, Ventricular
Cardiomyopathies
Ventricular Fibrillation
Support Vector Machine
Death, Sudden, Cardiac
Action Potentials
Electrophysiologic Techniques, Cardiac
Predictive Value of Tests
Signal Processing, Computer-Assisted
Risk Assessment
Diagnosis, Computer-Assisted
Time Factors
Analysis of Drug Effects on iPSC Cardiomyocytes with Machine Learning.
Annals of biomedical engineering
May 4, 2020
Patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer an attractive experimental platform to investigate cardiac diseases and therapeutic outcome. In this study, iPSC-CMs were utilized to study their calcium transient...
Application of a convolutional neural network for predicting the occurrence of ventricular tachyarrhythmia using heart rate variability features.
Scientific reports
Apr 21, 2020
Predicting the occurrence of ventricular tachyarrhythmia (VTA) in advance is a matter of utmost importance for saving the lives of cardiac arrhythmia patients. Machine learning algorithms have been used to predict the occurrence of imminent VTA. In t...
Assessment of ventricular tachyarrhythmia in patients with hypertrophic cardiomyopathy with machine learning-based texture analysis of late gadolinium enhancement cardiac MRI.
Diagnostic and interventional imaging
Nov 11, 2019
OBJECTIVE: To assess the diagnostic value of machine learning-based texture feature analysis of late gadolinium enhancement images on cardiac magnetic resonance imaging (MRI) for assessing the presence of ventricular tachyarrhythmia (VT) in patients ...
Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy (HCM-VAr-Risk Model).
The American journal of cardiology
Feb 27, 2019
Clinical risk stratification for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HC) employs rules derived from American College of Cardiology Foundation/American Heart Association (ACCF/AHA) guidelines or the HCM Risk-SCD model (C-index ∼...
Echocardiography, Stress
Magnetic Resonance Imaging, Cine
Predictive Value of Tests
Registries
Cardiomyopathy, Hypertrophic
Humans
Tachycardia, Ventricular
Middle Aged
Risk Assessment
Female
Electrocardiography
Reproducibility of Results
Machine Learning
Risk Factors
Prognosis
Male
Retrospective Studies
Electronic Health Records
Robotic magnetic catheter navigation as a first step in the automation of the ablation procedures for ventricular arrhythmia.
International journal of cardiology
May 26, 2018
Computer-Assisted Mapping in Electrophysiology: Are the Machines Taking Over?
JACC. Clinical electrophysiology
Jul 17, 2017
A novel algorithm for ventricular arrhythmia classification using a fuzzy logic approach.
Australasian physical & engineering sciences in medicine
Nov 4, 2016
In the present study, it has been shown that an unnecessary implantable cardioverter-defibrillator (ICD) shock is often delivered to patients with an ambiguous ECG rhythm in the overlap zone between ventricular tachycardia (VT) and ventricular fibril...
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