ArticleMedical physics2025
Automatic joint inflammation estimation based on regression neural networks.
Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in rheumatoid arthritis: current applications and future perspectives.Frontiers in medicine · 2026Pooled it
- Prevention of rheumatoid arthritis: state-of-the-art and considerations for the design of new trials.EULAR rheumatology open · 2026Review
- Automatic joint inflammation estimation based on regression neural networks.Medical physics · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundQuantitative 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. PURPOSE: 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.
methodsWe 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.
resultsThe 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.
conclusionsResults 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.