Deep learning-based automatic left atrial appendage filling defects assessment on cardiac computed tomography for clinical and subclinical atrial fibrillation patients

RATIONALE AND OBJECTIVES: Selecting region of interest (ROI) for left atrial appendage (LAA) filling defects assessment can be time consuming and prone to subjectivity. This study aimed to develop and validate a novel artificial intelligence (AI), deep learning (DL) based framework for automatic fil...

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Detalles Bibliográficos
Autores principales: Chen, Ling, Huang, Sung-Hao, Wang, Tzu-Hsiang, Lan, Tzuo-Yun, Tseng, Vincent S., Tsao, Hsuan-Ming, Wang, Hsueh-Han, Tang, Gau-Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868534/
https://www.ncbi.nlm.nih.gov/pubmed/36699283
http://dx.doi.org/10.1016/j.heliyon.2023.e12945

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868534/
https://www.ncbi.nlm.nih.gov/pubmed/36699283
http://dx.doi.org/10.1016/j.heliyon.2023.e12945

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