  Understanding the internal physiological changes accompanying the aging
process is an important aspect of medical image interpretation, with the
expected changes acting as a baseline when reporting abnormal findings. Deep
learning has recently been demonstrated to allow the accurate estimation of
patient age from chest X-rays, and shows potential as a health indicator and
mortality predictor. In this paper we present a novel comparative study of the
relative performance of radiologists versus state-of-the-art deep learning
models on two tasks: (a) patient age estimation from a single chest X-ray, and
(b) ranking of two time-separated images of the same patient by age. We train
our models with a heterogeneous database of 1.8M chest X-rays with ground truth
patient ages and investigate the limitations on model accuracy imposed by
limited training data and image resolution, and demonstrate generalisation
performance on public data. To explore the large performance gap between the
models and humans on these age-prediction tasks compared with other
radiological reporting tasks seen in the literature, we incorporate our age
prediction model into a conditional Generative Adversarial Network (cGAN)
allowing visualisation of the semantic features identified by the prediction
model as significant to age prediction, comparing the identified features with
those relied on by clinicians.
