  Purpose: To develop a machine learning model to classify the severity grades
of pulmonary edema on chest radiographs.
  Materials and Methods: In this retrospective study, 369,071 chest radiographs
and associated radiology reports from 64,581 (mean age, 51.71; 54.51% women)
patients from the MIMIC-CXR chest radiograph dataset were included. This
dataset was split into patients with and without congestive heart failure
(CHF). Pulmonary edema severity labels from the associated radiology reports
were extracted from patients with CHF as four different ordinal levels: 0, no
edema; 1, vascular congestion; 2, interstitial edema; and 3, alveolar edema.
Deep learning models were developed using two approaches: a semi-supervised
model using a variational autoencoder and a pre-trained supervised learning
model using a dense neural network. Receiver operating characteristic curve
analysis was performed on both models.
  Results: The area under the receiver operating characteristic curve (AUC) for
differentiating alveolar edema from no edema was 0.99 for the semi-supervised
model and 0.87 for the pre-trained models. Performance of the algorithm was
inversely related to the difficulty in categorizing milder states of pulmonary
edema (shown as AUCs for semi-supervised model and pre-trained model,
respectively): 2 versus 0, 0.88 and 0.81; 1 versus 0, 0.79 and 0.66; 3 versus
1, 0.93 and 0.82; 2 versus 1, 0.69 and 0.73; and, 3 versus 2, 0.88 and 0.63.
  Conclusion: Deep learning models were trained on a large chest radiograph
dataset and could grade the severity of pulmonary edema on chest radiographs
with high performance.
