News Release

Exploring the role of AI in early RA

Can a computer replace human interpretation?

Peer-Reviewed Publication

European Alliance of Associations for Rheumatology (EULAR)

Early inflammatory arthritis is often undifferentiated, but it may develop into established RA or another arthropathy.1 Alternatively, it may resolve spontaneously, or remain undifferentiated for indefinite periods. Erosion is a key prognostic factor which can be detected with magnetic resonance imaging (MRI).2 In addition, MRI allows direct visualization and assessment of (teno-) synovitis and bone marrow edema.3

Predicting early RA from MRI images of the hands and feet can help people access timely treatment, which may possibly change the disease course. Traditionally this is done by radiologists and rheumatologists using a scoring sheet to manually identify key features from the MRI scans. But now, artificial intelligence (AI) interpretations of MRI images could provide more accurate predictions than visual scoring by medical staff.

An abstract presented by Li and colleagues from Leiden University Medical Center details how deep-learning AI can automatically analyze scans in order to predict RA at an early stage in patients with clinically suspect arthralgia. The model was first trained to understand anatomy, then to distinguish patients from healthy controls, and finally to find image features predictive of RA development. The AI analyzed scans of the hands and feet from 1,974 people with either early-onset arthritis or clinically suspect arthralgia, of whom 651 went on to develop RA. Results from a held-out test set showed the model could predict RA with accuracies close to those achieved by human experts. This worked for scans of either hands or feet.

The authors conclude that AI interpretation of MRI scans could provide automatic RA prediction. Further training for the model using MRI data from healthy controls will probably improve the accuracy, and future research will focus on predicting RA in specifically undifferentiated arthritis, as a subgroup of the early onset arthritis group. Additionally, this new method not only confirms the significance of known inflammatory features such as synovial inflammation, but in the long term may also be able to identify new imaging biomarkers, further enhancing our understanding of the underlying disease process in early RA.


Li Y, et al. Exploring the Use of Artificial Intelligence in Predicting Rheumatoid Arthritis, Based on Extremity MR Scans in Early Arthritis and Clinically Suspect Arthralgia patients. Presented at EULAR 2023; Abstract OP0002.


1. Combe B, et al. 2016 update of the EULAR recommendations for the management of early arthritis. Ann Rheum Dis 2017;76:948–59.

2. Matthijssen, XME, et al. A search to the target tissue in which RA-specific inflammation starts: a detailed MRI study to improve identification of RA-specific features in the phase of clinically suspect arthralgia. Arthritis Res Ther 2019;21:249.

3. Østergaard M, et al. An introduction to the EULAR–OMERACT rheumatoid arthritis MRI reference image atlas. Ann Rheum Dis 2005;64 Suppl 1(Suppl 1):i3–7.


EULAR is the European umbrella organisation representing scientific societies, health professional associations and organisations for people with rheumatic and musculoskeletal diseases (RMDs). EULAR aims to reduce the impact of RMDs on individuals and society, as well as improve RMD treatments, prevention, and rehabilitation. To this end, EULAR fosters excellence in rheumatology education and research, promotes the translation of research advances into daily care, and advocates for the recognition of the needs of those living with RMDs by EU institutions.


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