ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2023
Deep learning-based joint detection in Rheumatoid arthritis hand radiographs.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
5 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
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 2025Review
- 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
- RA-XTNet: A Novel CNN Model to Predict Rheumatoid Arthritis from Hand Radiographs and Thermal Images: A Comparison with CNN Transformer and Quantum Computing.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advancements in technology have enabled diverse tools and medical devices that are able to improve the efficiency of diagnosis and detection of various health diseases. Rheumatoid arthritis is an autoimmune disease that affects multiple joints including the wrist, hands and feet. We used YOLOv5l6 to detect these joints in radiograph images. In this paper, we show that training YOLOv5l6 on joint images of healthy patients is able to achieve a high performance when used to evaluate joint images of patients with rheumatoid arthritis, even when there is a limited number of training samples. In addition to training joint images from healthy individuals with YOLOv5l6, we added several data augmentation steps to further improve the generalization of the deep learning model.
Identifiers
37350925PMC10283127What 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.