Improved detection of air trapping on expiratory computed tomography using deep learning

BACKGROUND: Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy fo...

Descripción completa

Detalles Bibliográficos
Autores principales: Ram, Sundaresh, Hoff, Benjamin A., Bell, Alexander J., Galban, Stefanie, Fortuna, Aleksa B., Weinheimer, Oliver, Wielpütz, Mark O., Robinson, Terry E., Newman, Beverley, Vummidi, Dharshan, Chughtai, Aamer, Kazerooni, Ella A., Johnson, Timothy D., Han, MeiLan K., Hatt, Charles R., Galban, Craig J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990199/
https://www.ncbi.nlm.nih.gov/pubmed/33760861
http://dx.doi.org/10.1371/journal.pone.0248902

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990199/
https://www.ncbi.nlm.nih.gov/pubmed/33760861
http://dx.doi.org/10.1371/journal.pone.0248902

Ejemplares similares