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

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

<h4>Background</h4>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...

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Bibliographic Details
Main Authors: Sundaresh Ram, Benjamin A Hoff, Alexander J Bell, Stefanie Galban, Aleksa B Fortuna, Oliver Weinheimer, Mark O Wielpütz, Terry E Robinson, Beverley Newman, Dharshan Vummidi, Aamer Chughtai, Ella A Kazerooni, Timothy D Johnson, MeiLan K Han, Charles R Hatt, Craig J Galban
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-01-01
Series:PLoS ONE
Online Access:https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248902&type=printable

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