Cargando…
Accuracy of Deep Learning Echocardiographic View Classification in Patients with Congenital or Structural Heart Disease: Importance of Specific Datasets
Introduction: Automated echocardiography image interpretation has the potential to transform clinical practice. However, neural networks developed in general cohorts may underperform in the setting of altered cardiac anatomy. Methods: Consecutive echocardiographic studies of patients with congenital...
Autores principales: | Wegner, Felix K., Benesch Vidal, Maria L., Niehues, Philipp, Willy, Kevin, Radke, Robert M., Garthe, Philipp D., Eckardt, Lars, Baumgartner, Helmut, Diller, Gerhard-Paul, Orwat, Stefan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8836991/ https://www.ncbi.nlm.nih.gov/pubmed/35160148 http://dx.doi.org/10.3390/jcm11030690 |
Ejemplares similares
-
Incidence and Predictors of Left Atrial Appendage Thrombus before Catheter Ablation of Thrombogenic Arrhythmias
por: Wegner, Felix K., et al.
Publicado: (2022) -
Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease
por: Diller, Gerhard-Paul, et al.
Publicado: (2020) -
Incidence and predictors of left atrial appendage thrombus on transesophageal echocardiography before elective cardioversion
por: Wegner, Felix K., et al.
Publicado: (2022) -
Adult congenital heart disease and the COVID-19 pandemic
por: Radke, Robert M, et al.
Publicado: (2020) -
Quantification of biventricular myocardial function using cardiac magnetic resonance feature tracking, endocardial border delineation and echocardiographic speckle tracking in patients with repaired tetralogy of fallot and healthy controls
por: Kempny, Aleksander, et al.
Publicado: (2012)