AI in Rheumatoid Arthritis

Artificial Intelligence (AI), of course, is sweeping across healthcare, as it is other industries.

Now two independent efforts to utilize AI predict the development of early rheumatoid arthritis (RA) from patients with signs and symptoms not meeting full disease criteria showed near expert-level accuracy,

The results of the studies were presented at the European Alliance of Associations for Rheumatology (EULAR) 2023 Annual Meeting.

Researchers from Leiden University Medical Center in the Netherlands developed an AI-based method to automatically analyze MR scans of extremities in order to predict early RA. And a study from a Japanese research team that used machine learning to create a model capable of predicting progression from undifferentiated arthritis (UA) to RA.

It appears that both approaches could enable early diagnosis of RA, which could lead to both swifter TTD (Time to Diagnosis) and TTI (Time to Treatment Differentiation), and improved clinical outcomes.

From the Netherlands study–

Results On the test set, the proposed model obtained a mean AUC of 0.683 in the EAC group, and 0.727 in the CSA group, using MR scans of the hands (wrist and metacarpophalangeal joints). Models trained separately on the wrists, MCPs and feet, received a mean AUC of 0.679, 0.647, 0.664, and 0.688, 0.669, 0.715, for the EAC and CSA group, respectively. These accuracies were close to the expert-level using RAMRIS, with reported AUCs of 0.74 and 0.69 in predicting RA in CSA. According to the proposed visualization method, the deep learning models predict RA, based on very similar patterns of known (teno-)synovial inflammation and bone marrow edema.