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...

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Bibliografische gegevens
Gepubliceerd in:PLoS One
Hoofdauteurs: 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.
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: Public Library of Science 2021
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Research Article
Online toegang:https://ncbi.nlm.nih.gov/pmc/articles/PMC7990199/
https://ncbi.nlm.nih.gov/pubmed/33760861
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0248902
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