Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy

AIMS: This study aims to identify and visualize electrocardiogram (ECG) features using an explainable deep learning–based algorithm to predict cardiac resynchronization therapy (CRT) outcome. Its performance is compared with current guideline ECG criteria and QRS(AREA). METHODS AND RESULTS: A deep l...

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Detalles Bibliográficos
Autores principales: Wouters, Philippe C, van de Leur, Rutger R, Vessies, Melle B, van Stipdonk, Antonius M W, Ghossein, Mohammed A, Hassink, Rutger J, Doevendans, Pieter A, van der Harst, Pim, Maass, Alexander H, Prinzen, Frits W, Vernooy, Kevin, Meine, Mathias, van Es, René
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Clinical Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9940988/
https://www.ncbi.nlm.nih.gov/pubmed/36342291
http://dx.doi.org/10.1093/eurheartj/ehac617

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9940988/
https://www.ncbi.nlm.nih.gov/pubmed/36342291
http://dx.doi.org/10.1093/eurheartj/ehac617

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