Radiologist-supervised Transfer Learning Improving Radiographic Localization of Pneumonia and Prognostication of Patients With COVID-19

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dc.contributor.authorHurt, B.
dc.contributor.authorRubel, M.A.
dc.contributor.authorMasutani, E.M.
dc.contributor.authorJacobs, K.
dc.contributor.authorHahn, L.
dc.contributor.authorHorowitz, M.
dc.contributor.authorKligerman, S.
dc.contributor.authorHsiao, A.
dc.date.issued2022
dc.description.abstractPurpose: To assess the potential of a transfer learning strategy leveraging radiologist supervision to enhance convolutional neural network-based (CNN) localization of pneumonia on radiographs and to further assess the prognostic value of CNN severity quantification on patients evaluated for COVID-19 pneumonia, for whom severity on the presenting radiograph is a known predictor of mortality and intubation. Materials and Methods: We obtained an initial CNN previously trained to localize pneumonia along with 25,684 radiographs used for its training. We additionally curated 1466 radiographs from patients who had a computed tomography (CT) performed on the same day. Regional likelihoods of pneumonia were then annotated by cardiothoracic radiologists, referencing these CTs. Combining data, a preexisting CNN was fine-tuned using transfer learning. Whole-image and regional performance of the updated CNN was assessed using receiver-operating characteristic area under the curve and Dice. Finally, the value of CNN measurements was assessed with survival analysis on 203 patients with COVID-19 and compared against modified radiographic assessment of lung edema (mRALE) score. Results: Pneumonia detection area under the curve improved on both internal (0.756 to 0.841) and external (0.864 to 0.876) validation data. Dice overlap also improved, particularly in the lung bases (R: 0.121 to 0.433, L: 0.111 to 0.486). There was strong correlation between radiologist mRALE score and CNN fractional area of involvement (ρ= 0.85). Survival analysis showed similar, strong prognostic ability of the CNN and mRALE for mortality, likelihood of intubation, and duration of hospitalization among patients with COVID-19. Conclusions: Radiologist-supervised transfer learning can enhance the ability of CNNs to localize and quantify the severity of disease. Closed-loop systems incorporating radiologists may be beneficial for continued improvement of artificial intelligence algorithms.
dc.description.statementofresponsibilityBrian Hurt, Meagan A. Rubel, Evan M. Masutani, Kathleen Jacobs, Lewis Hahn, Michael Horowitz, Seth Kligerman, and Albert Hsiao
dc.identifier.citationJournal of Thoracic Imaging, 2022; 37(2):90-99
dc.identifier.doi10.1097/RTI.0000000000000618
dc.identifier.issn0883-5993
dc.identifier.issn1536-0237
dc.identifier.orcidHorowitz, M. [0000-0002-0942-0306]
dc.identifier.urihttps://hdl.handle.net/2440/145865
dc.language.isoen
dc.publisherLippincott, Williams & Wilkins
dc.rights© 2021 Wolters Kluwer Health, Inc. All rights reserved.
dc.source.urihttp://dx.doi.org/10.1097/rti.0000000000000618
dc.subjecttransfer learning; COVID-19; artificial intelligence; chest radiograph; chest computed tomography; patient outcomes; closed loop; radiograph
dc.titleRadiologist-supervised Transfer Learning Improving Radiographic Localization of Pneumonia and Prognostication of Patients With COVID-19
dc.typeJournal article
pubs.publication-statusPublished

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