ArticleRheumatology advances in practice2019
Development and validation of a deep-learning model for scoring of radiographic finger joint destruction in rheumatoid arthritis.
Article in Rheumatology advances in practice, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed, 73 citations in OpenAlex.
- Deep learning-based classification of interphalangeal finger joints in erosive hand osteoarthritis.Osteoarthritis and cartilage open · 2026Article
- Use of Artificial Intelligence in Rheumatoid Arthritis: Advancements and Novel Perspectives.Journal of clinical medicine · 2026Review
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- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 2025Review
- Deep learning enables automatic detection of joint damage progression in rheumatoid arthritis-model development and external validation.Rheumatology (Oxford, England) · 2025Article
- Multistage deep learning methods for automating radiographic sharp score prediction in rheumatoid arthritis.Scientific reports · 2025Article
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- Can eye-tracking help to create a new method for X-ray analysis of rheumatoid arthritis patients, including joint segmentation and scoring methods?PLOS digital health · 2024Article
- Detection and Grading of Radiographic Hand Osteoarthritis Using an Automated Machine Learning Platform.ACR open rheumatology · 2024Article
- Ensemble detection of hand joint ankylosis and subluxation in radiographic images using deep neural networks.Scientific reports · 2024Article
- Deep learning discrimination of rheumatoid arthritis from osteoarthritis on hand radiography.Skeletal radiology · 2024Article
- What have we learnt from the inhibition of IL-6 in RA and what are the clinical opportunities for patient outcomes?Therapeutic advances in musculoskeletal disease · 2024Review
- Medimatrix: innovative pre-training of grayscale images for rheumatoid arthritis diagnosis revolutionises medical image classification.Health information science and systems · 2023Article
- Enchondroma Detection from Hand Radiographs with an Interactive Deep Learning Segmentation Tool-A Feasibility Study.Journal of clinical medicine · 2023Article
- Automatic evaluation of atlantoaxial subluxation in rheumatoid arthritis by a deep learning model.Arthritis research & therapy · 2023Article
- A survey of artificial intelligence in rheumatoid arthritis.Rheumatology and immunology research · 2023Article
- Development of a scoring model for the Sharp/van der Heijde score using convolutional neural networks and its clinical application.Rheumatology (Oxford, England) · 2023Article
- A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe purpose of this research was to develop a deep-learning model to assess radiographic finger joint destruction in RA.
methodsThe model comprises two steps: a joint-detection step and a joint-evaluation step. Among 216 radiographs of 108 patients with RA, 186 radiographs were assigned to the training/validation dataset and 30 to the test dataset. In the training/validation dataset, images of PIP joints, the IP joint of the thumb or MCP joints were manually clipped and scored for joint space narrowing (JSN) and bone erosion by clinicians, and then these images were augmented. As a result, 11 160 images were used to train and validate a deep convolutional neural network for joint evaluation. Three thousand seven hundred and twenty selected images were used to train machine learning for joint detection. These steps were combined as the assessment model for radiographic finger joint destruction. Performance of the model was examined using the test dataset, which was not included in the training/validation process, by comparing the scores assigned by the model and clinicians.
resultsThe model detected PIP joints, the IP joint of the thumb and MCP joints with a sensitivity of 95.3% and assigned scores for JSN and erosion. Accuracy (percentage of exact agreement) reached 49.3-65.4% for JSN and 70.6-74.1% for erosion. The correlation coefficient between scores by the model and clinicians per image was 0.72-0.88 for JSN and 0.54-0.75 for erosion.
conclusionImage processing with the trained convolutional neural network model is promising to assess radiographs in RA.
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