MultiCOVID: a multi modal deep learning approach for COVID-19 diagnosis

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  • dc.contributor.author Hardy-Werbin, Max
  • dc.contributor.author Maiques Llácer, José María
  • dc.contributor.author Busto Barrera, Marcos
  • dc.contributor.author Cirera Lorenzo, Isabel
  • dc.contributor.author Aguirre Tejedo, Alfons
  • dc.contributor.author García Gisbert, Nieves, 1994-
  • dc.contributor.author Zuccarino, Flavio
  • dc.contributor.author Carbullanca Toledo, Santiago
  • dc.contributor.author Carpio, Luis Alexander del
  • dc.contributor.author Ramal, Didac
  • dc.contributor.author Gayete, Ángel
  • dc.contributor.author Martínez Roldán, Jordi
  • dc.contributor.author Márquez Colomé, Albert
  • dc.contributor.author Bellosillo Paricio, Beatriz
  • dc.contributor.author Gibert Fernandez, Joan, 1988-
  • dc.date.accessioned 2024-06-03T06:29:59Z
  • dc.date.available 2024-06-03T06:29:59Z
  • dc.date.issued 2023
  • dc.description.abstract The rapid spread of the severe acute respiratory syndrome coronavirus 2 led to a global overextension of healthcare. Both Chest X-rays (CXR) and blood test have been demonstrated to have predictive value on Coronavirus Disease 2019 (COVID-19) diagnosis on different prevalence scenarios. With the objective of improving and accelerating the diagnosis of COVID-19, a multi modal prediction algorithm (MultiCOVID) based on CXR and blood test was developed, to discriminate between COVID-19, Heart Failure and Non-COVID Pneumonia and healthy (Control) patients. This retrospective single-center study includes CXR and blood test obtained between January 2017 and May 2020. Multi modal prediction models were generated using opensource DL algorithms. Performance of the MultiCOVID algorithm was compared with interpretations from five experienced thoracic radiologists on 300 random test images using the McNemar-Bowker test. A total of 8578 samples from 6123 patients (mean age 66 ± 18 years of standard deviation, 3523 men) were evaluated across datasets. For the entire test set, the overall accuracy of MultiCOVID was 84%, with a mean AUC of 0.92 (0.89-0.94). For 300 random test images, overall accuracy of MultiCOVID was significantly higher (69.6%) compared with individual radiologists (range, 43.7-58.7%) and the consensus of all five radiologists (59.3%, P < .001). Overall, we have developed a multimodal deep learning algorithm, MultiCOVID, that discriminates among COVID-19, heart failure, non-COVID pneumonia and healthy patients using both CXR and blood test with a significantly better performance than experienced thoracic radiologists.
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Hardy-Werbin M, Maiques JM, Busto M, Cirera I, Aguirre A, Garcia-Gisbert N, et al. MultiCOVID: a multi modal deep learning approach for COVID-19 diagnosis. Sci Rep. 2023 Oct 31;13(1):18761. DOI: 10.1038/s41598-023-46126-8
  • dc.identifier.doi http://dx.doi.org/10.1038/s41598-023-46126-8
  • dc.identifier.issn 2045-2322
  • dc.identifier.uri http://hdl.handle.net/10230/60327
  • dc.language.iso eng
  • dc.publisher Nature Research
  • dc.relation.ispartof Sci Rep. 2023 Oct 31;13(1):18761
  • dc.rights © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • dc.subject.keyword Biomarkers
  • dc.subject.keyword Computational biology and bioinformatics
  • dc.subject.keyword Diseases
  • dc.subject.keyword Health care
  • dc.title MultiCOVID: a multi modal deep learning approach for COVID-19 diagnosis
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion

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