ArticleScientific reports2022
Improved diagnosis of rheumatoid arthritis using an artificial neural network.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 45 citations in OpenAlex.
- Artificial intelligence in rheumatoid arthritis: current applications and future perspectives.Frontiers in medicine · 2026Pooled it
- Sex bias consideration in healthcare machine-learning research: a systematic review in rheumatoid arthritis.BMJ open · 2025Pooled it
- Automatic acromegaly detection using deep learning on hand images: a multicenter observational study.The Journal of clinical endocrinology and metabolism · 2026Observational
- Synovial microenvironment and fluorescence imaging for early rheumatoid arthritis diagnosis.Journal of pharmaceutical analysis · 2026Review
- An interpretable machine learning tool for rheumatoid arthritis screening: integrating novel cellular morphological parameters with routine blood count indices.Clinical rheumatology · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Identification of key factors for early detection of rheumatoid arthritis in primary care using machine learning.Scientific reports · 2026Article
- Review
- Decreased ALKBH5 in neutrophil correlates with disease activity in rheumatoid arthritis and ALKBH5 modulates neutrophil autophagy.Scientific reports · 2025Article
- The renin-angiotensin system (RAS) and arthritic diseases: therapeutic potential for RAS inhibitors.Inflammopharmacology · 2025Review
- Knowledge, Attitude, and Practice of Artificial Intelligence (AI) among Rheumatology Professionals: An Online Cross-Sectional Survey.Mediterranean journal of rheumatology · 2025Article
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Multistage deep learning methods for automating radiographic sharp score prediction in rheumatoid arthritis.Scientific reports · 2025Article
- Review
- 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
- Artificial intelligence in rheumatology: perspectives and insights from a nationwide survey of U.S. rheumatology fellows.Rheumatology international · 2024Article
- 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
- 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
- A survey of artificial intelligence in rheumatoid arthritis.Rheumatology and immunology research · 2023Article
- Artificial intelligence in rheumatoid arthritis: potential applications and future implications.Frontiers in medicine · 2023Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rheumatoid arthritis (RA) is chronic systemic disease that can cause joint damage, disability and destructive polyarthritis. Current diagnosis of RA is based on a combination of clinical and laboratory features. However, RA diagnosis can be difficult at its disease onset on account of overlapping symptoms with other arthritis, so early recognition and diagnosis of RA permit the better management of patients. In order to improve the medical diagnosis of RA and evaluate the effects of different clinical features on RA diagnosis, we applied an artificial neural network (ANN) as the training algorithm, and used fivefold cross-validation to evaluate its performance. From each sample, we obtained data on 6 features: age, sex, rheumatoid factor, anti-citrullinated peptide antibody (CCP), 14-3-3η, and anti-carbamylated protein (CarP) antibodies. After training, this ANN model assigned each sample a probability for being either an RA patient or a non-RA patient. On the validation dataset, the F1 for all samples by this ANN model was 0.916, which was higher than the 0.906 we previously reported using an optimal threshold algorithm. Therefore, this ANN algorithm not only improved the accuracy of RA diagnosis, but also revealed that anti-CCP had the greatest effect while age and anti-CarP had a weaker on RA diagnosis.
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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.