Machine learning in the detection and management of atrial fibrillation.
Arrhythmia
Artificial intelligence
Deep learning
Electrophysiology
Neural network
Journal
Clinical research in cardiology : official journal of the German Cardiac Society
ISSN: 1861-0692
Titre abrégé: Clin Res Cardiol
Pays: Germany
ID NLM: 101264123
Informations de publication
Date de publication:
Sep 2022
Sep 2022
Historique:
received:
11
01
2022
accepted:
16
03
2022
pubmed:
31
3
2022
medline:
1
9
2022
entrez:
30
3
2022
Statut:
ppublish
Résumé
Machine learning has immense novel but also disruptive potential for medicine. Numerous applications have already been suggested and evaluated concerning cardiovascular diseases. One important aspect is the detection and management of potentially thrombogenic arrhythmias such as atrial fibrillation. While atrial fibrillation is the most common arrhythmia with a lifetime risk of one in three persons and an increased risk of thromboembolic complications such as stroke, many atrial fibrillation episodes are asymptomatic and a first diagnosis is oftentimes only reached after an embolic event. Therefore, screening for atrial fibrillation represents an important part of clinical practice. Novel technologies such as machine learning have the potential to substantially improve patient care and clinical outcomes. Additionally, machine learning applications may aid cardiologists in the management of patients with already diagnosed atrial fibrillation, for example, by identifying patients at a high risk of recurrence after catheter ablation. We summarize the current state of evidence concerning machine learning and, in particular, artificial neural networks in the detection and management of atrial fibrillation and describe possible future areas of development as well as pitfalls. Typical data flow in machine learning applications for atrial fibrillation detection.
Identifiants
pubmed: 35353207
doi: 10.1007/s00392-022-02012-3
pii: 10.1007/s00392-022-02012-3
pmc: PMC9424134
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
1010-1017Informations de copyright
© 2022. The Author(s).
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