Automated quantification of wrist bone marrow oedema, pre- and post-treatment, in early rheumatoid arthritis.

Objective: Bone inflammation (osteitis) in early RA (ERA) manifests as bone marrow oedema (BME) and precedes the development of bone erosion. In this prospective, single-centre study, we developed an automated post-processing pipeline for quantifying the severity of wrist BME on T2-weighted fat-suppressed MRI.

Methods: A total of 80 ERA patients [mean age 54 years (s.d. 12), 62 females] were enrolled at baseline and 49 (40 females) after 1 year of treatment. For automated bone segmentation, a framework based on a convolutional neural network (nnU-Net) was trained and validated (5-fold cross-validation) for 15 wrist bone areas at baseline in 60 ERA patients. For BME quantification, BME was identified by Gaussian mixture model clustering and thresholding. BME proportion (%) and relative BME intensity within each bone area were compared with visual semi-quantitative assessment of the RA MRI score (RAMRIS).

Results: For automated wrist bone area segmentation, overall bone Sørensen-Dice similarity coefficient was 0.91 (s.d. 0.02) compared with ground truth manual segmentation. High correlation (Pearson correlation coefficient r = 0.928, P < 0.001) between visual RAMRIS BME and automated BME proportion assessment was found. The automated BME proportion decreased after treatment, correlating highly (r = 0.852, P < 0.001) with reduction in the RAMRIS BME score.

Conclusion: The automated model developed had an excellent segmentation performance and reliable quantification of both the proportion and relative intensity of wrist BME in ERA patients, providing a more objective and efficient alternative to RAMRIS BME scoring.