ArticleArthritis research & therapy2023
Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting.
Article in Arthritis research & therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Diagnostic and prognostic performance of artificial intelligence and radiomics in ankylosing spondylitis: a systematic review and meta-analysis.Journal of robotic surgery · 2026Pooled it
- Ankylosing Spondylitis Presenting as a Severe Kyphotic Deformity Without Pain: A Case Report and Literature Review.Cureus · 2026Article
- Progression to Radiographic Axial Spondyloarthritis: A Narrative Review on Timeline and Novel Prediction Factors.International journal of molecular sciences · 2026Review
- Article
- AI-Powered Lateral DEXA Morphometry for Integrated Evaluation of Thoracic Kyphosis and Bone Density Assessment in Patients with Axial Spondyloarthritis.Life (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Application of Artificial Intelligence in rheumatic disease classification: an example of ankylosing spondylitis severity inspection model.Annals of medicine · 2025Article
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 2025Review
- 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
- Machine learning and deep learning for the diagnosis and treatment of ankylosing spondylitis- a scoping review.Journal of clinical orthopaedics and trauma · 2024Article
- Entry point of machine learning in axial spondyloarthritis.RMD open · 2024Review
- Precision medicine in axial spondyloarthritis: current opportunities and future perspectives.Therapeutic advances in musculoskeletal disease · 2024Review
- Beyond the horizon: Innovations and future directions in axial-spondyloarthritis.Archives of rheumatology · 2023Review
- Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting radiographic progression in axial spondyloarthritis (axSpA) remains limited because of the complex interaction between multiple associated factors and individual variability in real-world settings. Hence, we tested the feasibility of artificial neural network (ANN) models to predict radiographic progression in axSpA.
methodsIn total, 555 patients with axSpA were split into training and testing datasets at a 3:1 ratio. A generalized linear model (GLM) and ANN models were fitted based on the baseline clinical characteristics and treatment-dependent variables for the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS) of the radiographs at follow-up time points. The mSASSS prediction was evaluated, and explainable machine learning methods were used to provide insights into the model outcome or prediction.
resultsThe R
conclusionsClinical and radiographic data-driven ANN allows precise mSASSS prediction in real-world settings. Correct evaluation and prediction of spinal structural changes could be beneficial for monitoring patients with axSpA and developing a treatment plan.
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