  Most convolutional neural networks in chest radiology use only the frontal
posteroanterior (PA) view to make a prediction. However the lateral view is
known to help the diagnosis of certain diseases and conditions. The recently
released PadChest dataset contains paired PA and lateral views, allowing us to
study for which diseases and conditions the performance of a neural network
improves when provided a lateral x-ray view as opposed to a frontal
posteroanterior (PA) view. Using a simple DenseNet model, we find that using
the lateral view increases the AUC of 8 of the 56 labels in our data and
achieves the same performance as the PA view for 21 of the labels. We find that
using the PA and lateral views jointly doesn't trivially lead to an increase in
performance but suggest further investigation.
