  Knee Osteoarthritis (OA) is a debilitating disease affecting over 250 million
people worldwide. Currently, radiologists grade the severity of OA on an
ordinal scale from zero to four using the Kellgren-Lawrence (KL) system. Recent
studies have raised concern in relation to the subjectivity of the KL grading
system, highlighting the requirement for an automated system, while also
indicating that five ordinal classes may not be the most appropriate approach
for assessing OA severity. This work presents preliminary results of an
automated system with a continuous grading scale. This system, namely
SS-FewSOME, uses self-supervised pre-training to learn robust representations
of the features of healthy knee X-rays. It then assesses the OA severity by the
X-rays' distance to the normal representation space. SS-FewSOME initially
trains on only 'few' examples of healthy knee X-rays, thus reducing the
barriers to clinical implementation by eliminating the need for large training
sets and costly expert annotations that existing automated systems require. The
work reports promising initial results, obtaining a positive Spearman Rank
Correlation Coefficient of 0.43, having had access to only 30 ground truth
labels at training time.
