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

ABSTRACT:

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 necessitate exploring novel solutions. Artificial intelligence (AI) models show promise in addressing this gap, enabling early intervention. This study assessed the effectiveness of AI in detecting AF using baseline sinus rhythm-ECG (SR-ECG) and factors influencing their performance.

Methods

A systematic review was conducted across eight databases and registries (International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY) registration: INPLASY202530059). References up to May 2024 were double-screened for el

SUBMITTER: Tsiartas E 

PROVIDER: S-EPMC12581083 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature