Explainable paroxysmal atrial fibrillation diagnosis using an artificial intelligence-enabled electrocardiogram.

Journal: The Korean journal of internal medicine

PMID: 39987899

Abstract

BACKGROUND/AIMS: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG).

Authors

Yeongbong Jin

Department of Industrial Engineering, Seoul National University, Seoul, Korea.
Bonggyun Ko

Department of Mathematics and Statistics, Chonnam National University, Gwangju, Korea.
Woojin Chang

Department of Industrial Engineering, Seoul National University, Seoul, Korea.
Kang-Ho Choi

Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, Korea (the Republic of) jhbt0607@hanmail.net ckhchoikang@hanmail.net.
Ki Hong Lee

The Heart Center of Chonnam National University Hospital, 42 Jaebongro, Dong-gu, Gwangju 501-757, South Korea.

Keywords

Action Potentials Aged Artificial Intelligence Atrial Fibrillation Deep Learning Early Diagnosis Electrocardiography Female Humans Male Middle Aged Neural Networks, Computer Predictive Value of Tests

External Resources

View on PubMed Access via DOI PubMed (39987899)

Explainable paroxysmal atrial fibrillation diagnosis using an artificial intelligence-enabled electrocardiogram.

Abstract

Authors

Keywords

External Resources

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