Artificial intelligence for the detection, prediction, and management of atrial fibrillation

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Isaksen, Jonas L., Baumert, Mathias, Hermans, Astrid N. L., Maleckar, Molly, Linz, Dominik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Medizin 2022
Materias:
Schwerpunkt
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8853037/
https://www.ncbi.nlm.nih.gov/pubmed/35147766
http://dx.doi.org/10.1007/s00399-022-00839-x
Descripción
Sumario: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.

Ejemplares similares