  Age prediction based on appearances of different anatomies in medical images
has been clinically explored for many decades. In this paper, we used deep
learning to predict a persons age on Chest X-Rays. Specifically, we trained a
CNN in regression fashion on a large publicly available dataset. Moreover, for
interpretability, we explored activation maps to identify which areas of a CXR
image are important for the machine (i.e. CNN) to predict a patients age,
offering insight. Overall, amongst correctly predicted CXRs, we see areas near
the clavicles, shoulders, spine, and mediastinum being most activated for age
prediction, as one would expect biologically. Amongst incorrectly predicted
CXRs, we have qualitatively identified disease patterns that could possibly
make the anatomies appear older or younger than expected. A further technical
and clinical evaluation would improve this work. As CXR is the most commonly
requested imaging exam, a potential use case for estimating age may be found in
the preventative counseling of patient health status compared to their
age-expected average, particularly when there is a large discrepancy between
predicted age and the real patient age.
