  Rheumatoid arthritis is an autoimmune disease that causes joint damage due to
inflammation in the soft tissue lining the joints known as the synovium. It is
vital to identify joint damage as soon as possible to provide necessary
treatment early and prevent further damage to the bone structures. Radiographs
are often used to assess the extent of the joint damage. Currently, the scoring
of joint damage from the radiograph takes expertise, effort, and time. Joint
damage associated with rheumatoid arthritis is also not quantitated in clinical
practice and subjective descriptors are used. In this work, we describe a
pipeline of deep learning models to automatically identify and score rheumatoid
arthritic joint damage from a radiographic image. Our automatic tool was shown
to produce scores with extremely high balanced accuracy within a couple of
minutes and utilizing this would remove the subjectivity of the scores between
human reviewers.
