ArticleJournal of the Royal Society, Interface2014
A computational method to differentiate normal individuals, osteoarthritis and rheumatoid arthritis patients using serum biomarkers.
Article in Journal of the Royal Society, Interface, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Pooled it
- Association of Circulating Vascular Endothelial Growth Factor Levels With Autoimmune Diseases: A Systematic Review and Meta-Analysis.Frontiers in immunology · 2021Pooled it
- Transforming cytokine diagnostics: AI, multiplexing, and point-of-care biosensing technologies.Mikrochimica acta · 2025Review
- AI-Assisted Plasmonic Diagnostics Platform for Osteoarthritis and Rheumatoid Arthritis With Biomarker Quantification Using Mathematical Models.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Predicting multiple sclerosis prognosis using AI and machine learning: integrating clinical, immunological, and radiological variables.Frontiers in neurology · 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
- Development and Validation of a Cost-Effective Machine Learning Model for Screening Potential Rheumatoid Arthritis in Primary Healthcare Clinics.Journal of inflammation research · 2025Article
- Medimatrix: innovative pre-training of grayscale images for rheumatoid arthritis diagnosis revolutionises medical image classification.Health information science and systems · 2023Article
- Unique Serum Immune Phenotypes and Stratification of Oklahoma Native American Rheumatic Disease Patients.Arthritis care & research · 2023Article
- Biomarkers of Osteoarthritis-A Narrative Review on Causal Links with Metabolic Syndrome.Life (Basel, Switzerland) · 2023Review
- Artificial intelligence in rheumatoid arthritis: potential applications and future implications.Frontiers in medicine · 2023Review
- Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review.Rheumatology and therapy · 2022Review
- Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers.Arthritis research & therapy · 2022Article
- A multicentre validation study of a smartphone application to screen hand arthritis.BMC musculoskeletal disorders · 2022Article
- Detection and Evaluation of Serological Biomarkers to Predict Osteoarthritis in Anterior Cruciate Ligament Transection Combined Medial Meniscectomy Rat Model.International journal of molecular sciences · 2021Article
- Machine Learning in Rheumatic Diseases.Clinical reviews in allergy & immunology · 2021Review
- Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis.Frontiers in pharmacology · 2021Review
- Knee Osteoarthritis: A Review of Pathogenesis and State-Of-The-Art Non-Operative Therapeutic Considerations.Genes · 2020Review
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
- Serum and synovial fluid cytokine profiling in hip osteoarthritis: distinct from knee osteoarthritis and correlated with pain.BMC musculoskeletal disorders · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of this study was to develop a method for categorizing normal individuals (normal, n = 100) as well as patients with osteoarthritis (OA, n = 100), and rheumatoid arthritis (RA, n = 100) based on a panel of inflammatory cytokines expressed in serum samples. Two panels of inflammatory proteins were used as training sets in the construction of two separate artificial neural networks (ANNs). The first training set consisted of all proteins (38 in total) and the second consisted of only the significantly different proteins expressed (12 in total) between at least two patient groups. Both ANNs obtained high levels of sensitivity and specificity, with the first and second ANN each diagnosing 100% of test set patients correctly. These results were then verified by re-investigating the entire dataset using a decision tree algorithm. We show that ANNs can be used for the accurate differentiation between serum samples of patients with OA, a diagnosed RA patient comparator cohort and normal/control cohort. Using neural network and systems biology approaches to manage large datasets derived from high-throughput proteomics should be further explored and considered for diagnosing diseases with complex pathologies.
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.