Deep Learning–based Fibrosis Extent on Computed Tomography Predicts Outcome of Fibrosing Interstitial Lung Disease Independent of Visually Assessed Computed Tomography Pattern

Author:

Oh Andrea S.1ORCID,Lynch David A.2,Swigris Jeffrey J.3,Baraghoshi David4,Dyer Debra S.2,Hale Valerie A.2,Koelsch Tilman L.2,Marrocchio Cristina5,Parker Katherine N.2,Teague Shawn D.2,Flaherty Kevin R.6,Humphries Stephen M.2ORCID

Affiliation:

1. Department of Radiology, University of California, Los Angeles, Los Angeles, California;

2. Department of Radiology,

3. Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, and

4. Department of Biostatistics, National Jewish Health, Denver, Colorado;

5. Department of Radiology, University of Trieste, Trieste, Italy; and

6. Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Michigan, Ann Arbor, Michigan

Funder

Pulmonary Fibrosis Foundation

Publisher

American Thoracic Society

Subject

Pulmonary and Respiratory Medicine

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