Cargando…
Feasibility of artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis in the current clinical environment: An online survey
FUNDING ACKNOWLEDGEMENTS: Type of funding sources: Foundation. Main funding source(s): The study was funded by a University of Edinburgh Wellcome Trust iTPA award. The authors acknowledge the support of the British Heart Foundation Centre for Research Excellence Award III (RE/18/5/34216). SEW is sup...
Autores principales: | Bodagh, N, Ali, O, Kotadia, I, Sim, I, Gharaviri, A, Mozaffar, H, Cresswell, K, Solis-Lemus, J, Baptiste, T, Corrado, C, Niederer, S, O'neill, M, Williams, S E |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10206823/ http://dx.doi.org/10.1093/europace/euad122.533 |
Ejemplares similares
-
Explainable Paroxysmal Atrial Fibrillation Diagnosis Using Electrocardiogram with Artificial Intelligence
por: Lee, K H, et al.
Publicado: (2023) -
Assessment of the atrial fibrillation burden in Holter ECG recordings using artificial intelligence
por: Hennings, E, et al.
Publicado: (2023) -
Deep learning to diagnose left ventricular hypertrophy from standard, 12-lead ECG signals: a proof-of-concept study
por: Pantelidis, P, et al.
Publicado: (2023) -
Atrial fibrillation detection using the ECG signal in the left in ear region: validation study on patients electively admitted for DC cardioversion
por: De Lucia, R, et al.
Publicado: (2023) -
Machine learning approach on high risk treadmill exercise test to predict obstructive coronary artery disease by using P QRS and T waves features
por: Yilmaz, A Y, et al.
Publicado: (2023)