Rethinking Knee Osteoarthritis Severity Grading: A Few Shot Self-Supervised Contrastive Learning Approach
This work addresses the need for more objective and scalable grading of knee osteoarthritis, a debilitating disease affecting over 250 million people, though it is incremental as it builds on self-supervised and contrastive learning methods.
The paper tackled the problem of subjective and ordinal grading of knee osteoarthritis severity by developing an automated system with a continuous scale, achieving a Spearman correlation of 0.43 using only 30 labeled examples.
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.