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.
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.
Authors: John R Zech; Marcus A Badgeley; Manway Liu; Anthony B Costa; Joseph J Titano; Eric Karl Oermann Journal: PLoS Med Date: 2018-11-06 Impact factor: 11.069
Authors: L Zhang; F Zhu; L Xie; C Wang; J Wang; R Chen; P Jia; H Q Guan; L Peng; Y Chen; P Peng; P Zhang; Q Chu; Q Shen; Y Wang; S Y Xu; J P Zhao; M Zhou Journal: Ann Oncol Date: 2020-03-26 Impact factor: 32.976
Authors: Pranav Rajpurkar; Jeremy Irvin; Robyn L Ball; Kaylie Zhu; Brandon Yang; Hershel Mehta; Tony Duan; Daisy Ding; Aarti Bagul; Curtis P Langlotz; Bhavik N Patel; Kristen W Yeom; Katie Shpanskaya; Francis G Blankenberg; Jayne Seekins; Timothy J Amrhein; David A Mong; Safwan S Halabi; Evan J Zucker; Andrew Y Ng; Matthew P Lungren Journal: PLoS Med Date: 2018-11-20 Impact factor: 11.069
Authors: Jordan H Chamberlin; Gilberto Aquino; Uwe Joseph Schoepf; Sophia Nance; Franco Godoy; Landin Carson; Vincent M Giovagnoli; Callum E Gill; Liam J McGill; Jim O'Doherty; Tilman Emrich; Jeremy R Burt; Dhiraj Baruah; Akos Varga-Szemes; Ismail M Kabakus Journal: Acad Radiol Date: 2022-04-04 Impact factor: 5.482