ArticleClinical medicine insights. Arthritis and musculoskeletal disorders2026
Automated Diagnosis of Rheumatoid Arthritis From Hand Radiographs Using Artificial Intelligence: A Retrospective Study.
Article in Clinical medicine insights. Arthritis and musculoskeletal disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Rheumatoid arthritis (RA) is a chronic inflammatory disease that damages hand and wrist joints, leading to pain, disability, and reduced quality of life. Radiographic assessment plays a key role in diagnosis, but it is subjective and dependent on the clinician's experience. Deep learning-based systems offer the potential for faster, more objective, and more consistent evaluation. Objectives: This study aims to develop an attention-based deep learning model for the automated diagnosis of RA from hand and wrist radiographs and to demonstrate that high diagnostic performance can be achieved even with a limited data set. Design: Retrospective observational study evaluating an attention-based deep learning model for automated diagnosis of RA from hand and wrist radiographs. Methods: Radiographs from 311 RA patients and 259 healthy controls collected between September 2018 and September 2024 were analyzed. Individuals with other conditions causing hand deformities were excluded. The data set was divided into training (n = 325), validation (n = 142), and test (n = 50) sets. DenseNet121 and DenseNet169 architectures were combined with an attention mechanism to highlight RA-specific structural changes. Despite the relatively small data set, data augmentation and attention modeling were used to improve robustness. Results: The proposed model achieved 88% accuracy, 84% precision, and 91% recall, demonstrating strong diagnostic capability with limited training data. Initial clinical testing suggests that the model can support radiologists by providing consistent and objective assessments. Conclusion: This attention-based deep learning approach shows promise as an effective, reliable, and efficient tool for the automated diagnosis of RA. The ability to achieve high performance with limited data highlights its potential for real-world clinical adoption, particularly in resource-constrained environments.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.