AIMC Topic: Electrophysiologic Techniques, Cardiac
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Research on noninvasive electrophysiologic imaging based on cardiac electrophysiology simulation and deep learning methods for the inverse problem.
BMC cardiovascular disorders
Apr 28, 2025
BACKGROUND: The risk stratification and prognosis of cardiac arrhythmia depend on the individual condition of patients, while invasive diagnostic methods may be risky to patient health, and current non-invasive diagnostic methods are applicable to fe...
Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning.
Circulation. Arrhythmia and electrophysiology
Feb 10, 2025
BACKGROUND: It is difficult to identify patients with atrial fibrillation (AF) most likely to respond to ablation. While any arrhythmia patient may recur after acutely successful ablation, AF is unusual in that patients may have long-term arrhythmia ...
Machine learning for prediction of ventricular arrhythmia episodes from intracardiac electrograms of automatic implantable cardioverter-defibrillators.
Heart rhythm
May 24, 2024
BACKGROUND: Despite effectiveness of the implantable cardioverter-defibrillator (ICD) in saving patients with life-threatening ventricular arrhythmias (VAs), the temporal occurrence of VA after ICD implantation is unpredictable.
Time to capitalise on artificial intelligence in cardiac electrophysiology.
Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing
Apr 11, 2024
Impact of artificial intelligence arrhythmia mapping on time to first ablation, procedure duration, and fluoroscopy use.
Journal of cardiovascular electrophysiology
Mar 4, 2024
INTRODUCTION: Artificial intelligence (AI) ECG arrhythmia mapping provides arrhythmia source localization using 12-lead ECG data; whether this information impacts procedural efficiency is unknown. We performed a retrospective, case-control study to e...
Time-to-Treatment
Electrophysiologic Techniques, Cardiac
Arrhythmias, Cardiac
Heart Rate
Catheter Ablation
Operative Time
Predictive Value of Tests
Fluoroscopy
Male
Middle Aged
Case-Control Studies
Treatment Outcome
Action Potentials
Artificial Intelligence
Time Factors
Humans
Retrospective Studies
Electrocardiography
Female
Aged
Image-Decomposition-Enhanced Deep Learning for Detection of Rotor Cores in Cardiac Fibrillation.
IEEE transactions on bio-medical engineering
Dec 22, 2023
OBJECTIVE: Rotors, regions of spiral wave reentry in cardiac tissues, are considered as the drivers of atrial fibrillation (AF), the most common arrhythmia. Whereas physics-based approaches have been widely deployed to detect the rotors, in-depth kno...
Atrial fibrillation signatures on intracardiac electrograms identified by deep learning.
Computers in biology and medicine
Apr 1, 2022
BACKGROUND: Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy.
Edge-enhancement densenet for X-ray fluoroscopy image denoising in cardiac electrophysiology procedures.
Medical physics
Jan 18, 2022
PURPOSE: Reducing X-ray dose increases safety in cardiac electrophysiology procedures but also increases image noise and artifacts which may affect the discernibility of devices and anatomical cues. Previous denoising methods based on convolutional n...
The Role of Artificial Intelligence and Machine Learning in Clinical Cardiac Electrophysiology.
The Canadian journal of cardiology
Jul 29, 2021
In recent years, numerous applications for artificial intelligence (AI) in cardiology have been found, due in part to large digitized data sets and the evolution of high-performance computing. In the discipline of cardiac electrophysiology (EP), a nu...
A deep learning algorithm to translate and classify cardiac electrophysiology.
eLife
Jul 2, 2021
The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) has been a critical in vitro advance in the study of patient-specific physiology, pathophysiology, and pharmacology. We designed a new deep learning multitask network ...
Phenethylamines
Myocytes, Cardiac
Gene Expression Regulation
Electrophysiological Phenomena
Humans
Cell Differentiation
Algorithms
Deep Learning
Induced Pluripotent Stem Cells
Models, Biological
Computer Simulation
Action Potentials
Electrophysiologic Techniques, Cardiac
Sulfonamides
ERG1 Potassium Channel
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