Generative adversarial network for automatic quantification of Coronavirus disease 2019 pneumonia on chest radiographs

Abstract

Purpose: To develop a generative adversarial network (GAN) to quantify COVID-19 pneumonia on chest radiographs automatically. Materials and Methods: This retrospective study included 50,000 consecutive non-COVID-19 chest CT scans in 2015–2017 for training. Anteroposterior virtual chest, lung, and pneumonia radiographs were generated from whole, segmented lung, and pneumonia pixels from each CT scan. Two GANs were sequentially trained to generate lung images from radiographs and to generate pneumonia images from lung images. GAN-driven pneumonia extent (pneumonia area/lung area) was expressed from 0% to 100%. We examined the correlation of GAN-driven pneumonia extent with semi-quantitative Brixia X-ray severity score (one dataset, n = 4707) and quantitative CT-driven pneumonia extent (four datasets, n = 54–375), along with analyzing a measurement difference between the GAN and CT extents. Three datasets (n = 243–1481), where unfavorable outcomes (respiratory failure, intensive care unit admission, and death) occurred in 10%, 38%, and 78%, respectively, were used to examine the predictive power of GAN-driven pneumonia extent. Results: GAN-driven radiographic pneumonia was correlated with the severity score (0.611) and CT-driven extent (0.640). 95% limits of agreements between GAN and CT-driven extents were −27.1% to 17.4%. GAN-driven pneumonia extent provided odds ratios of 1.05–1.18 per percent for unfavorable outcomes in the three datasets, with areas under the receiver operating characteristic curve (AUCs) of 0.614–0.842. When combined with demographic information only and with both demographic and laboratory information, the prediction models yielded AUCs of 0.643–0.841 and 0.688–0.877, respectively. Conclusion: The generative adversarial network automatically quantified COVID-19 pneumonia on chest radiographs and identified patients with unfavorable outcomes.