Radiologist-supervised Transfer Learning: Improving Radiographic Localization of Pneumonia and Prognostication of Patients With COVID-19. Issue 2 (28th March 2022)

• Record Type: Journal Article
• Authors: Hurt, Brian; Rubel, Meagan A.; Masutani, Evan M.; Jacobs, Kathleen; Hahn, Lewis; Horowitz, Michael; Kligerman, Seth; Hsiao, Albert
• Is Part Of: Journal of thoracic imaging. Volume 37:Issue 2(2022)
• Page Start: 90
• Page End: 99
• Publication Date: 2022-03-28
• Subjects: transfer learning -- COVID-19 -- artificial intelligence -- chest radiograph -- chest computed tomography -- patient outcomes -- closed loop -- radiograph; Chest -- Radiography -- Periodicals; Chest -- Diseases -- Diagnosis -- Periodicals; 617.540757
• DOI: 10.1097/RTI.0000000000000618
• Languages: English
• ISSNs: 0883-5993

Abstract

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.