A deep learning-based comparative MRI model to detect inflammatory changes in rheumatoid arthritis

Abstract

Rheumatoid Arthritis (RA) is an autoimmune disease that mainly affects joints in the wrist and hands. It typically results in inflamed and painful joints. MRI is one of the most common imaging modalities to detect and monitor possible inflamed RA-related areas, enabling rheumatologists to treat patients more timely and efficiently. Despite the importance of finding and tracking inflamed areas associated with RA in MRI, there is no previously published work on finding pixel-by-pixel changes related to RA between baseline and follow-up MRIs. Therefore, this paper proposes a hypothesis-free deep learning-based model to discover changes in wrist MRIs on a pixel level to detect changes in inflamed areas related to RA without using prior anatomical information. To do this, a combination of a U-Net-based network and image thresholding was utilised to find pixel-level non-trivial changes between baseline and follow-up MRI images. A wrist MRI dataset including 99 individual pairs of MRI images (each pair constructed of baseline and follow-up images) was used to evaluate the proposed model. Data were collected from patients with clinically suspected arthralgia (CSA), defined as patients at risk of developing RA according to their rheumatologist and already had subclinical inflammation on MRI but could not be diagnosed with RA (yet) since they had not developed clinically detectable arthritis. The obtained results were evaluated using an observer study. The evaluation showed that our proposed model is a promising first step toward developing an automatic model to find RA-related inflammatory changes.