nnUnet-based automated quantification of wrist joint synovitis volume in patients with rheumatoid arthritis: a feasibility study

TL;DR

This study developed an automated AI model to measure synovitis volume in rheumatoid arthritis patients using MRI scans, showing promising accuracy and potential for clinical use.

Contribution

A novel nnU-Net-based model for automated synovitis volume quantification in RA patients, validated with cross-center data.

Findings

The automated model achieved a Sørensen-Dice similarity coefficient of 0.75 compared to manual segmentation.

The model showed excellent agreement with manual measurements (Pearson correlation r = 0.975).

Performance remained consistent with external data from different imaging centers (DSC = 0.70).

Abstract

Synovitis is the key inflammatory feature of rheumatoid arthritis (RA). Quantitative assessment of synovitis better correlates with patient outcomes than semiquantitative assessment but it is time-consuming.

To develop and validate an automated model for segmentation and quantification of wrist synovial tissue volume on postcontrast fat-suppressed T1-weighted MRI.

Patients with early RA (symptoms for ≤24 months) at a single center were recruited at baseline and were followed up at year 1 and year 8. Postcontrast axial fat-suppressed T1-weighted images of the most symptomatic wrist were acquired at 3.0 T. One observer manually segmented consecutive synovitis areas on all MRI datasets. A framework, based on the convolutional neural network, nnU-Net, was trained and validated (5-fold cross-validation with image level splits) with 295 image datasets used for model training and validation. The rheumatoid arthritis MRI score was used to semiquantitatively grade synovitis. Manually segmented synovial volume by a single reader was used as the reference standard. Forty-five external image datasets from 2 different imaging centers were used to test generalizable applicability.

For automated synovitis segmentation, the overall Sørensen-Dice similarity coefficient (DSC) was 0.75 ± 0.11 (mean ± SD) compared to manual segmentation. Higher DSC values were found in patients with moderate (0.80 ± 0.06) and severe (0.84 ± 0.05) degrees of synovitis. The model had a similar performance with externally acquired data (DSC value: 0.70 ± 0.20). Predicted and manually segmented synovitis volume measurements showed excellent agreement (Pearson correlation: r = 0.975, P < .001).

A fully automated model quantified wrist synovial tissue volume with good agreement to manual reference and maintained performance on external data, supporting potential use in clinical studies and prospective evaluation in practice.

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