presents an exciting opportunity to improve the availability, latency,
accuracy, and consistency of chest X-ray (CXR) image interpretation.
Indeed, a plethora of algorithms have already been developed to detect
specific conditions, such as lung cancer, tuberculosis and pneumothorax.
By virtue of being trained to detect a specific disease, however, the
utility of these algorithms may be limited in a general clinical setting,
where a wide variety of abnormalities could surface. For example, a
pneumothorax detector is not expected to highlight nodules suggestive of
cancer, and a tuberculosis detector may not identify findings specific to
pneumonia.
In “Deep Learning for Distinguishing Normal versus Abnormal Chest
Radiographs and Generalization to Two Unseen Diseases Tuberculosis and
COVID-19”, published in Scientific Reports, we present a model that can
distinguish between normal and abnormal CXRs across multiple de-identified
datasets and settings. We find that the model performs well on general
abnormalities, as well as unseen examples of tuberculosis and COVID-19. We
are also releasing our set of radiologists’ labels1 for the test set used
in this study for the publicly available ChestX-ray14 dataset.
