Rheumatoid arthritis classification and prediction by consistency-based deep learning using extremity MRI scans

Li, Y.L.; Hassanzadeh, T.; Shamonin, D.P.; Reijnierse, M.; Mil, A.H.M.V.; Stoel, B.C. (2024)

Article / Letter to editor

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 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

• Rheumatoid arthritis
• Deep learning
• Self-supervised learning
• Wrist MRI

• All authors: Li, Y.L.; Hassanzadeh, T.; Shamonin, D.P.; Reijnierse, M.; Mil, A.H.M.V.; Stoel, B.C.
• Date: 2024-05-31
• Journal: Biomedical Signal Processing and Control
• Volume: 91
• DOI: doi:10.1016/j.bspc.2024.105990