Deep learning-based prediction of rheumatoid arthritis-associated deformity on MRI

While the prevalence of surgery to correct atlantoaxial subluxation (AAS), subaxial subluxation (SAS) and vertical translocation (VT) in patients with rheumatoid arthritis (RA) had declined, cervical deformity is still observed regularly.

The objective of this study is to develop a deep learning-based algorithm to predict RA-associated upper cervical spine deformity in patients before or close to RA diagnosis, with the purpose of early risk stratification.

Patients with RA in which follow-up cervical MRI studies (at least 3 years apart) were available were identified retrospectively in two tertiary care centers. Patients without definitive deformity at baseline were included in the algorithm. Patients were assessed for RA-associated cervical spine deformity, defined as presence of pannus and/or degeneration of the facet joints of C0-C1 and/or C1-C2 on follow up MRI.

Of 3248 patients identified, 220 patients were included in this study, of whom 33 patients developed cervical spine deformity. 153 patients were included for training and sixty-seven for validation of the deep learning-based prediction model. The accuracy of the model was 0.84, with a positive predictive value of 0.56 and a negative predictive value of 0.92.

A deep learning model was developed to predict the development of pannus and/or facet joint deformity at the craniocervical junction of patients with RA. Future research should focus on large-scale validation of this model with diverse sites and identifying the role of the subaxial spine in the risk of deformity at the level of the craniocervical junction during the course of disease.