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Authors
Baraghoshi, David https://orcid.org/0000-0002-9485-9267
Strand, Matthew J.
Humphries, Stephen M.
Lynch, David A.
Kaizer, Alexander M.
Porras, Antonio R.
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Funding
Funding for this research was provided by:
National Heart, Lung, and Blood Institute (U01 HL089897, U01 HL089856)
National Institutes of Health (75N92023D00011)
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Article History
Received: 23 October 2024
Revised: 29 January 2025
Accepted: 19 February 2025
First Online: 4 April 2025
Compliance with ethical standards
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: The scientific guarantor of this publication is David Baraghoshi.
: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
: Four of the authors have significant statistical expertise.
: Written informed consent was obtained from all subjects (patients) in this study.
: Institutional Review Board approval was obtained.
: Data for 1205 of the 1504 participants our study focused on have been previously reported (Hatt et al []). This prior article sought to understand the reproducibility of conventional quantitative CT metrics of emphysema between variable dose protocols, whereas, in this manuscript, we aimed to develop deep learning methods to increase consistency between variable dose protocols and provide measures of prediction reliability.
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