AIMC Topic: Atrial Fibrillation

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Early detection of paroxysmal atrial fibrillation from non-episodic ECG data using cardiac dynamics features and different classification models.

Biomedical physics & engineering express
Intelligent computer-aided diagnosis techniques enable inspection of invisible electrocardiogram (ECG) pathological changes for early detection of latent heart diseases. This study concentrates on latent pathological changes within non-episodic ECG d...

Targeted nanodelivery strategies for atrial fibrillation: concomitant targeting of fibrosis suppression and electrical conduction restoration through advanced nanobiotechnology.

Journal of nanobiotechnology
Atrial fibrillation (AF), the most common form of cardiac arrhythmia, is closely associated with atrial fibrosis and electrophysiological remodeling, both of which contribute to its persistence and recurrence. Current treatment strategies remain limi...

Molecular mechanisms of lipid metabolism abnormalities driving sepsis and atrial fibrillation: A Systematic study based on bioinformatics and machine learning.

PloS one
BACKGROUND: Sepsis and atrial fibrillation are complex, life-threatening medical conditions affecting approximately 49 million individuals globally, characterized by exceptionally high mortality rates. Lipid metabolism abnormalities play a critical r...

Machine Learning and Arrhythmia: Advances in Atrial Fibrillation Detection and Management.

Current atherosclerosis reports
PURPOSE OF REVIEW: In this paper we review recent advancements in the diagnosis and management of atrial fibrillation through machine learning (ML).

Artificial intelligence capabilities in identifying atrial fibrillation using baseline sinus rhythm ECG : a systematic review.

Open heart
BACKGROUND: Atrial fibrillation (AF) is a prevalent arrhythmia associated with adverse outcomes, often presenting paroxysmally. The lack of an efficient method to promptly detect paroxysmal AF and the absence of a unified screening approach necessita...

Universal Atrial Fibrillation Screening Using Electrocardiographic Artificial Intelligence: A Cost-Effective Approach in Rural Communities.

Journal of medical systems
Atrial fibrillation (AF) significantly contributes to the incidence of strokes. Screening for AF enhances its detection and effective management. However, universal AF screening in rural areas poses a challenge. This study evaluates the cost-effectiv...

Incidence and severity of aortic stenosis according to machine learning predicted risk of atrial fibrillation.

Scientific reports
Atrial fibrillation (AF) and aortic stenosis (AS) are two common progressive conditions affecting older persons that share pathobiological pathways. Early detection of AS is critical for improving outcomes, but no prediction tool exists to inform dec...