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Machine Learning-enhanced X-ray-based Radiomics in the Identification of Post-COVID Patients

Crossref DOI link: https://doi.org/10.1016/j.arbres.2024.12.004

Published Print: 2025-04

Update policy: https://doi.org/10.1016/elsevier_cm_policy

Authors

Fulop, Andrea

Gil, Jeovanis

Rozsas, Anita

Solymosi, Diana

Bogos, Krisztina

Ferencz, Bence

Berta, Judit

Dome, Peter

Marko-Varga, Gyorgy

Dome, Balazs

Kerpel-Fronius, Anna

Megyesfalvi, Zsolt
Funding

Funding for this research was provided by:

Austrian Science Fund (I3522, I3977, I4677)

Nemzeti Kutatási Fejlesztési és Innovációs Hivatal (FK-143751, TKP2021-EGA-33)

Innovációs és Technológiai Minisztérium (UNKP-20-3, UNKP-21-3, UNKP-23-5)

Hungarian Academy of Sciences (KH130356, PC2022-II-19/1/2022)

International Association for the Study of Lung Cancer

Semmelweis University
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Text and Data Mining valid from 2025-04-01

Text and Data Mining valid from 2025-04-01

Version of Record valid from 2024-12-18
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This article is maintained by: Elsevier

Article Title: Machine Learning-enhanced X-ray-based Radiomics in the Identification of Post-COVID Patients

Journal Title: Archivos de Bronconeumología

CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.arbres.2024.12.004

Content Type: simple-article

Copyright: © 2024 The Authors. Published by Elsevier España, S.L.U. on behalf of SEPAR.