Predicting the development of rheumatoid arthritis (RA) in an early stage through magnetic resonance imaging (MRI) can initiate timely treatment and improve long-term patient outcomes. Although... Show morePredicting the development of rheumatoid arthritis (RA) in an early stage through magnetic resonance imaging (MRI) can initiate timely treatment and improve long-term patient outcomes. Although manual prediction is time-consuming and requires expert knowledge, automatic RA prediction has not been fully investigated. While standard models fail to achieve acceptable performance, we present a consistency-based deep learning framework to classify and predict RA automatically and precisely, including an output-standardized model, customized self-supervised pretraining and a loss function that is based on label consistency between original and augmented inputs. For training and evaluation, we used a database, containing 5945 MRI scans of carpal, metacarpophalangeal (MCP), and metatarsophalangeal (MTP) joints, from 2151 subjects obtained over a period of ten years. Four (three classification- and one prediction-) tasks were defined to distinguish two patient groups (with recent-onset arthritis and clinically suspect arthralgia) from healthy controls and RA from other arthritis patients within the recent-onset arthritis group, and predict RA development in a period of two years within the clinically suspect arthralgia group. The proposed method was evaluated with the area under the receiver operating curve (AUROC) on a separate test set, achieving mean AUROCs of 83.6%, 83.3%, and 69.7% in the three classification tasks, and 67.8% in the prediction task. This proves the existence of early signs of RA in MRI and the potential of a consistency-based deep learning model to detect these early signs and predict RA Show less
Hassanzadeh, T.; Shamonin, D.P.; Li, Y.L.; Krijbolder, D.I.; Reijnierse, M.; Helm-van Mil, A.H.M.V. van der; Stoel, B.C. 2024
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...Show moreRheumatoid 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. Show less
