AIMC Topic: Cardiac Resynchronization Therapy
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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
Ventricular Remodeling
Image Interpretation, Computer-Assisted
Electrocardiography
Treatment Outcome
Support Vector Machine
Predictive Value of Tests
Signal Processing, Computer-Assisted
Patient Selection
Heart Failure
Cardiac Resynchronization Therapy
Male
Ventricular Function, Left
Echocardiography
Middle Aged
Female
Aged
Aged, 80 and over
New Multiparametric Analysis of Cardiac Dyssynchrony: Machine Learning and Prediction of Response to CRT.
JACC. Cardiovascular imaging
Apr 17, 2019
Ventricular Function, Left
Echocardiography
Electrocardiography
Treatment Outcome
Decision Support Techniques
Retrospective Studies
Clinical Decision-Making
Ventricular Dysfunction, Left
Image Interpretation, Computer-Assisted
Predictive Value of Tests
Humans
Signal Processing, Computer-Assisted
Machine Learning
Heart Failure
Cardiac Resynchronization Therapy
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...
Discovering and identifying New York heart association classification from electronic health records.
BMC medical informatics and decision making
Jul 23, 2018
BACKGROUND: Cardiac Resynchronization Therapy (CRT) is an established pacing therapy for heart failure patients. The New York Heart Association (NYHA) class is often used as a measure of a patient's response to CRT. Identifying NYHA class for heart f...
3D/2D model-to-image registration by imitation learning for cardiac procedures.
International journal of computer assisted radiology and surgery
May 12, 2018
PURPOSE: In cardiac interventions, such as cardiac resynchronization therapy (CRT), image guidance can be enhanced by involving preoperative models. Multimodality 3D/2D registration for image guidance, however, remains a significant research challeng...
Prescription of Guideline-Recommended Implantable Cardioverter Defibrillator and Cardiac Resynchronization Therapy Among Patients Hospitalized With Heart Failure and Varying Degrees of Renal Function.
The American journal of cardiology
Dec 18, 2016
Implantable cardioverter defibrillators (ICD) and cardiac resynchronization therapy (CRT) reduce mortality in many patients with heart failure (HF), but the current use and effectiveness of ICD/CRT in patients with chronic kidney disease (CKD) are un...
A framework for combining a motion atlas with non-motion information to learn clinically useful biomarkers: Application to cardiac resynchronisation therapy response prediction.
Medical image analysis
Oct 11, 2016
We present a framework for combining a cardiac motion atlas with non-motion data. The atlas represents cardiac cycle motion across a number of subjects in a common space based on rich motion descriptors capturing 3D displacement, velocity, strain and...
Using artificial intelligence-enabled electrocardiogram to predict cardiac resynchronization therapy outcomes of left bundle branch area pacing.
Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
Dec 28, 2023
Explainable AI for ECG-based prediction of cardiac resynchronization therapy outcomes: learning from machine learning?
European heart journal
Feb 21, 2023
Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy.
European heart journal
Feb 21, 2023
AIMS: This study aims to identify and visualize electrocardiogram (ECG) features using an explainable deep learning-based algorithm to predict cardiac resynchronization therapy (CRT) outcome. Its performance is compared with current guideline ECG cri...
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