Advances in Atrial Fibrillation: Mechanisms, Diagnosis, and Emerging Therapies

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Cardiology".

Deadline for manuscript submissions: 1 September 2026 | Viewed by 105

Special Issue Editors


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Guest Editor
Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy
Interests: atrial fibrillation; heart failure; cardiac arrhythmias

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Guest Editor
Texas Cardiac Arrhythmia Institute, St David's Medical Center, Austin, TX, USA
Interests: cardiac arrhythmias, atrial fibrillation; heart failure; arrhythmia; sudden cardiac death; catheter ablation
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Special Issue Information

Dear Colleagues,

Atrial fibrillation (AF) represents the most prevalent sustained cardiac arrhythmia in clinical practice, affecting millions worldwide and conferring a five-fold increased risk of ischemic stroke. Despite substantial progress in understanding its pathophysiology, AF continues to pose major diagnostic and therapeutic challenges. Several studies have demonstrated that AF can be detected in up to 30% of patients with embolic stroke of undetermined source (ESUS) during long-term follow-up, underscoring its critical but often elusive role in cerebrovascular disease.

Emerging evidence also suggests that AF is not always the direct cause of stroke but may serve as a marker of underlying atrial pathology. The concept of atrial cardiomyopathy, defined by structural, electrical, and functional abnormalities of the atrial myocardium, has gained prominence as a potential precursor to AF and stroke. Structural remodeling, altered electrophysiological properties, and delayed atrial conduction have been identified as early hallmarks of this condition, offering novel insights into preclinical disease states. Understanding the interplay between atrial remodeling and thromboembolic risk may enable the earlier identification of high-risk patients and the development of targeted preventive strategies.

Our aim in launching this Special Issue is to provide a comprehensive overview of the latest advances in the mechanisms, diagnosis, and treatment of AF. By integrating insights from clinical, translational, and basic research, we aim to bridge the gap between pathophysiological understanding and practical management. Topics of interest include novel biomarkers of atrial cardiomyopathy, innovative diagnostic tools such as wearable and implantable monitoring devices, and emerging therapeutic strategies that extend beyond traditional rate and rhythm control approaches.

Cutting-edge research contributions are encouraged, particularly studies that explore the molecular mechanisms underlying atrial remodeling, the role of inflammation and fibrosis in AF progression, and the application of artificial intelligence in arrhythmia detection and risk stratification.

We invite original research articles, systematic reviews, meta-analyses, and expert perspectives that advance the understanding of AF from its subclinical manifestations to advanced disease stages. By highlighting novel diagnostic modalities, therapeutic innovations, and preventive strategies, this Special Issue will foster multidisciplinary collaboration and guide the next generation of precision medicine approaches in atrial fibrillation.

Dr. Angelica Cersosimo
Dr. Vincenzo Mirco La Fazia
Guest Editors

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Keywords

  • cardiac arrhythmias
  • atrial fibrillation
  • heart failure
  • arrhythmia
  • sudden cardiac death
  • catheter ablation

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Published Papers (1 paper)

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Review

14 pages, 615 KB  
Review
Artificial Intelligence Applied to Electrocardiograms Recorded in Sinus Rhythm for Detection and Prediction of Atrial Fibrillation: A Scoping Review
by Ziga Mrak, Franjo Husam Naji and Dejan Dinevski
Medicina 2026, 62(1), 199; https://doi.org/10.3390/medicina62010199 (registering DOI) - 17 Jan 2026
Abstract
Background and Objectives: Subclinical paroxysmal atrial fibrillation (AF) is often undetected by conventional screening strategies, until complications emerge. Artificial intelligence (AI) applied to sinus rhythm electrocardiograms has emerged as a promising tool to identify individuals with occult AF and to predict the risk [...] Read more.
Background and Objectives: Subclinical paroxysmal atrial fibrillation (AF) is often undetected by conventional screening strategies, until complications emerge. Artificial intelligence (AI) applied to sinus rhythm electrocardiograms has emerged as a promising tool to identify individuals with occult AF and to predict the risk of future incident AF. This scoping review synthesizes evidence from original studies evaluating AI models trained on sinus rhythm ECGs for AF detection or AF prediction. Materials and Methods: A comprehensive search of MEDLINE, Embase, Web of Science, Scopus, and IEEE Xplore was conducted to identify peer-reviewed studies from inception to November 2025. Eligible studies included original investigations in which the model input was a sinus rhythm ECG and the outcome was either paroxysmal AF or new-onset AF. Extracted variables included cohort characteristics, ECG acquisition parameters, AI architecture, model predictive performance, AF prediction horizon, clinical outcomes, and validation strategy. Risk of bias was assessed using PROBAST. Results: Nineteen studies met the inclusion criteria. Retrospective datasets ranging from several thousand to over one million ECGs and convolutional or deep neural network AI architectures were used in most studies. AI-ECG models demonstrated high diagnostic accuracy for detecting subclinical AF (ten studies; AUROC 0.75–0.90) and for predicting long-term new-onset AF (six studies; AUROC 0.69–0.85) from a single sinus rhythm ECG. Robust external validation was reported in eleven studies. Combining AI-ECG models with clinical risk factors improved AF predictive performance in several reports. Key limitations across studies included retrospective design, patient selection, limited calibration reporting, and sparse prospective impact data. Conclusions: AI-based analysis of sinus rhythm ECGs can detect occult AF and stratify future AF risk with moderate-to-high accuracy across multiple populations and healthcare systems. However, rigorous prospective trials, evaluating clinical benefit, cost-effectiveness, calibration across demographic groups, and real-world implementation, are required before broad adoption in clinical practice. Full article
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