  Quantifying the degree of atrophy is done clinically by neuroradiologists
following established visual rating scales. For these assessments to be
reliable the rater requires substantial training and experience, and even then
the rating agreement between two radiologists is not perfect. We have developed
a model we call AVRA (Automatic Visual Ratings of Atrophy) based on machine
learning methods and trained on 2350 visual ratings made by an experienced
neuroradiologist. It provides fast and automatic ratings for Scheltens' scale
of medial temporal atrophy (MTA), the frontal subscale of Pasquier's Global
Cortical Atrophy (GCA-F) scale, and Koedam's scale of Posterior Atrophy (PA).
We demonstrate substantial inter-rater agreement between AVRA's and a
neuroradiologist ratings with Cohen's weighted kappa values of $\kappa_w$ =
0.74/0.72 (MTA left/right), $\kappa_w$ = 0.62 (GCA-F) and $\kappa_w$ = 0.74
(PA), with an inherent intra-rater agreement of $\kappa_w$ = 1. We conclude
that automatic visual ratings of atrophy can potentially have great clinical
and scientific value, and aim to present AVRA as a freely available toolbox.
