Radiologist-supervised Transfer Learning Improving Radiographic Localization of Pneumonia and Prognostication of Patients With COVID-19
Date
2022
Authors
Hurt, B.
Rubel, M.A.
Masutani, E.M.
Jacobs, K.
Hahn, L.
Horowitz, M.
Kligerman, S.
Hsiao, A.
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Journal article
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Journal of Thoracic Imaging, 2022; 37(2):90-99
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Brian Hurt, Meagan A. Rubel, Evan M. Masutani, Kathleen Jacobs, Lewis Hahn, Michael Horowitz, Seth Kligerman, and Albert Hsiao
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Abstract
Purpose: 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.
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