Exploring Atrial Fibrillation Through Computational Models: Clinical Applications, Mechanisms, and Advancements in Tools

Atrial fibrillation (AF) is the most common cardiac arrhythmia, characterized by a fast and irregular heart rate.Computer modelling has been playing an ever more important role in understanding AF.This review showcases research from the past two years from a variety of aspects relevant to computational modelling of AF.Beginning with research that is directly relevant in clinical settings, such as antiarrhythmic drugs, ablation techniques and blood clot modelling.This is followed by studies that aim to understand the underlying mechanisms behind AF, including influence from fibrosis, fatty tissue, and atrial model size and makeup.The last section features literature on techniques, methodologies and tools that are relevant to AF modelling, such as diffusion algorithms, imaging, and artificial intelligence.

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Publication Details

Journal
Computing in cardiology
Published
2024-12-01
DOI
https://doi.org/10.22489/cinc.2024.133
Primary Topic
Atrial Fibrillation Management and Outcomes
Type
article
Field-Weighted Citation Impact
0.00
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article

Exploring Atrial Fibrillation Through Computational Models: Clinical Applications, Mechanisms, and Advancements in Tools

James J. Kennelly, Jichao Zhao, Roshan Sharma
Computing in cardiology
Atrial Fibrillation Management and Outcomes
article

Exploring Atrial Fibrillation Through Computational Models: Clinical Applications, Mechanisms, and Advancements in Tools

James J. Kennelly, Jichao Zhao, Roshan Sharma
article en

Abstract

Atrial fibrillation (AF) is the most common cardiac arrhythmia, characterized by a fast and irregular heart rate.Computer modelling has been playing an ever more important role in understanding AF.This review showcases research from the past two years from a variety of aspects relevant to computational modelling of AF.Beginning with research that is directly relevant in clinical settings, such as antiarrhythmic drugs, ablation techniques and blood clot modelling.This is followed by studies that aim to understand the underlying mechanisms behind AF, including influence from fibrosis, fatty tissue, and atrial model size and makeup.The last section features literature on techniques, methodologies and tools that are relevant to AF modelling, such as diffusion algorithms, imaging, and artificial intelligence.

Computing in cardiologyVol. 51
Auckland Institute of Studies (NZ), University of Auckland (NZ), Auckland University of Technology (NZ)
Openalex Percentile: Top 58%
Atrial Fibrillation Management and Outcomes
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