  The growing demand for head magnetic resonance imaging (MRI) examinations,
along with a global shortage of radiologists, has led to an increase in the
time taken to report head MRI scans around the world. For many neurological
conditions, this delay can result in increased morbidity and mortality. An
automated triaging tool could reduce reporting times for abnormal examinations
by identifying abnormalities at the time of imaging and prioritizing the
reporting of these scans. In this work, we present a convolutional neural
network for detecting clinically-relevant abnormalities in
$\text{T}_2$-weighted head MRI scans. Using a validated neuroradiology report
classifier, we generated a labelled dataset of 43,754 scans from two large UK
hospitals for model training, and demonstrate accurate classification (area
under the receiver operating curve (AUC) = 0.943) on a test set of 800 scans
labelled by a team of neuroradiologists. Importantly, when trained on scans
from only a single hospital the model generalized to scans from the other
hospital ($\Delta$AUC $\leq$ 0.02). A simulation study demonstrated that our
model would reduce the mean reporting time for abnormal examinations from 28
days to 14 days and from 9 days to 5 days at the two hospitals, demonstrating
feasibility for use in a clinical triage environment.
