Artificial intelligence for the detection, prediction, and management of atrial fibrillation.
Erkennung, Vorhersage und Behandlung von Vorhofflimmern mithilfe künstlicher Intelligenz.
AF
AI
Deep learning
Disease management
Machine learning
Neural networks
Journal
Herzschrittmachertherapie & Elektrophysiologie
ISSN: 1435-1544
Titre abrégé: Herzschrittmacherther Elektrophysiol
Pays: Germany
ID NLM: 9425873
Informations de publication
Date de publication:
Mar 2022
Mar 2022
Historique:
received:
14
01
2022
accepted:
17
01
2022
pubmed:
12
2
2022
medline:
1
3
2022
entrez:
11
2
2022
Statut:
ppublish
Résumé
The present article reviews the state of the art of machine learning algorithms for the detection, prediction, and management of atrial fibrillation (AF), as well as of the development and evaluation of artificial intelligence (AI) in cardiology and beyond. Today, AI detects AF with a high accuracy using 12-lead or single-lead electrocardiograms or photoplethysmography. The prediction of paroxysmal or future AF currently operates at a level of precision that is too low for clinical use. Further studies are needed to determine whether patient selection for interventions may be possible with machine learning. In diesem Beitrag wird der aktuelle Stand von Algorithmen des maschinellen Lernens zur Erkennung, Vorhersage und Behandlung von Vorhofflimmern zusammengefasst, zudem werden die Entwicklung und Prüfung von künstlicher Intelligenz in der Kardiologie und anderen Bereichen dargelegt. Nach heutigem Stand lässt sich Vorhofflimmern mithilfe künstlicher Intelligenz in 12-Kanal- oder 1‑Kanal-Elektrokardiogrammen bzw. in Photoplethysmogrammen zuverlässig erkennen. Die Vorhersage von paroxysmalem oder neu auftretendem Vorhofflimmern hat die für den klinischen Einsatz erforderliche Genauigkeit noch nicht erreicht. Weitere Studien sind notwendig, um zu untersuchen, ob auf Basis des maschinellen Lernens eine Patientenselektion für Interventionen möglich ist.
Autres résumés
Type: Publisher
(ger)
In diesem Beitrag wird der aktuelle Stand von Algorithmen des maschinellen Lernens zur Erkennung, Vorhersage und Behandlung von Vorhofflimmern zusammengefasst, zudem werden die Entwicklung und Prüfung von künstlicher Intelligenz in der Kardiologie und anderen Bereichen dargelegt. Nach heutigem Stand lässt sich Vorhofflimmern mithilfe künstlicher Intelligenz in 12-Kanal- oder 1‑Kanal-Elektrokardiogrammen bzw. in Photoplethysmogrammen zuverlässig erkennen. Die Vorhersage von paroxysmalem oder neu auftretendem Vorhofflimmern hat die für den klinischen Einsatz erforderliche Genauigkeit noch nicht erreicht. Weitere Studien sind notwendig, um zu untersuchen, ob auf Basis des maschinellen Lernens eine Patientenselektion für Interventionen möglich ist.
Identifiants
pubmed: 35147766
doi: 10.1007/s00399-022-00839-x
pii: 10.1007/s00399-022-00839-x
pmc: PMC8853037
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
34-41Informations de copyright
© 2022. The Author(s).
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