Do Lateral Views Help Automated Chest X-ray Predictions?
This addresses a practical problem for medical imaging by evaluating if lateral views enhance diagnostic accuracy in automated systems, though it is incremental as it builds on existing datasets and models.
The study investigated whether adding lateral views improves automated chest X-ray predictions, finding that using lateral views increased AUC for 8 out of 56 labels and matched PA view performance for 21 labels, with no trivial gain from joint use.
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.