Automatic joint inflammation estimation based on regression neural networks

Quantitative assessment of inflammation from hand and forefoot MRI scans is crucial for evaluating the severity, progression, and treatment response in inflammatory disease like rheumatoid arthritis (RA). Traditionally, this relies on visual evaluation of signs like bone marrow edema (BME), tenosynovitis, and synovitis, which is time-consuming, subjective, and prone to inherent inter/intra-reader variability.

This study aims at an automatic DL-based MRI analysis of inflammatory signs in RA system for inflammation assessment to facilitate related diagnoses and studies.

We developed an Automatic DL-based MRI analysis of Inflammatory signs in RA (ADMIRA) system for inflammation assessment, using pre- and post-processing alongside DL models to estimate inflammation scores from fat saturated, contrast-enhanced T1-weighted MRI scans of 2254 subjects across four study populations. These MRI scans include three different anatomical sites, wrist, metacarpophalangeal (MCP) and metatarsophalangeal (MTP) joints, as the objects for inflammation assessment. The scans were divided into training, monitoring, testing and validation sets to ensure robust performance evaluation, using Pearson's correlation coefficients and Intra-class correlation coefficients. A revised class activation mapping (CAM) algorithm was used to validate the DL model's reliability, illustrating its inference process.

The system achieved mean R/ICCs of nearly 0.9 for synovitis and tenosynovitis on test sets and 0.8 on the validation set, with slightly lower scores for BME (0.8 and 0.7, respectively). This system presents a performance close to human experts on the same datasets. Meanwhile, the visualization results indicate the DL models have a inference process consistent with expert knowledge.

Results show that ADMIRA provides accurate, expert-level inflammation estimation, particularly for synovitis and tenosynovitis, offering a fast, reliable alternative to manual methods for RA monitoring and analysis. We expect that this automatic method could help to reduce labor costs and improve the efficiency of diagnosis in the future.