  The use of smartphones to take photographs of chest x-rays represents an
appealing solution for scaled deployment of deep learning models for chest
x-ray interpretation. However, the performance of chest x-ray algorithms on
photos of chest x-rays has not been thoroughly investigated. In this study, we
measured the diagnostic performance for 8 different chest x-ray models when
applied to photos of chest x-rays. All models were developed by different
groups and submitted to the CheXpert challenge, and re-applied to smartphone
photos of x-rays in the CheXphoto dataset without further tuning. We found that
several models had a drop in performance when applied to photos of chest
x-rays, but even with this drop, some models still performed comparably to
radiologists. Further investigation could be directed towards understanding how
different model training procedures may affect model generalization to photos
of chest x-rays.
