AI-Based Medical Data Analysis for Differentiating Inflammatory vs Degenerative Joint Diseases in Elderly Patients
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Arthritis, Rheumatoid (RA). Basic parameters: from 60 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Egypt
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Artificial Intelligence-Enhanced Medical Data Analysis for Differentiating Inflammatory and Degenerative Joint Diseases and Detecting of Disease Severity in Elderly Patient
Overview
This study aims to evaluate the diagnostic accuracy of AI-assisted imaging analysis in differentiating between inflammatory and degenerative joint diseases in elderly patients. The performance of AI-based analysis will be compared with radiologists' assessments to determine its reliability in clinical practice. In addition, the study will explore imaging features most predictive of each disease type using advanced machine learning techniques. Finally, the feasibility of implementing AI tools in the routine management of geriatric musculoskeletal disorders will be assessed.
Detailed description
Musculoskeletal disorders are among the most prevalent causes of disability in the elderly. Inflammatory joint diseases, such as rheumatoid arthritis, and degenerative joint diseases, such as osteoarthritis, are both common yet challenging to differentiate, particularly in the early stages. Traditional imaging techniques often lack sensitivity and specificity when interpreted solely by human experts, and diagnostic accuracy is further limited by inter-observer variability.
Artificial Intelligence (AI), particularly deep learning-based image analysis, has emerged as a powerful tool in medical diagnostics. Convolutional neural networks (CNNs), a class of deep learning models, have been successfully applied to musculoskeletal imaging. For example, a study published in The Lancet Rheumatology (2020) trained a CNN on thousands of hand and wrist radiographs from patients with rheumatoid arthritis. The model was able to automatically detect and grade bone erosions and joint space narrowing-key radiographic features of rheumatoid arthritis-with diagnostic performance comparable to experienced musculoskeletal radiologists. Importantly, AI was able to identify early erosive changes in small joints, reduce the time required for radiographic scoring in clinical trials, and provide consistent results, thereby reducing inter-observer variability.
Building on these advances, the current study aims to explore the application of AI in enhancing diagnostic accuracy for differentiating between inflammatory and degenerative joint diseases in elderly patients. By integrating AI-based imaging analysis with clinical and laboratory data, this research will not only support accurate diagnosis but also provide predictive models for disease course, functional decline, and joint damage progression. The ultimate goal is to enable personalized treatment strategies and improve outcomes for elderly patients with musculoskeletal disorders.
Primary outcome measures
- Diagnostic accuracy of AI system [Time frame: Within 12 months from baseline assessment.]
Eligibility criteria
Inclusion criteria
- Age ≥ 60 years
- Clinical suspicion or confirmed diagnosis of inflammatory joint disease (e.g., rheumatoid arthritis, psoriatic arthritis) or degenerative joint disease (e.g., osteoarthritis)
- Availability of relevant musculoskeletal imaging (X-rays, MRI, or ultrasound) suitable for AI-based analysis
- Ability to provide informed consent or have a legal representative consent on behalf of the
Exclusion criteria
- History of recent joint trauma (within the last 6 months) or previous joint surgery affecting the studied sites
- Presence of bone or joint malignancy (primary or metastatic)
- Diagnosis of overlapping rheumatologic syndromes or mixed pathology (e.g., RA with concurrent gout, or OA with inflammatory overlap)
- Inadequate imaging quality or absence of required imaging modalities
- Inability or unwillingness to provide informedconsent
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Case-only
Study locations
Egypt · 1 center
- Assiut University Hospital — Asyut
Publications
- Bhaumik S. Cardiologists are putting in stents needlessly, doctors say. BMJ. 2013 Feb 4;346:f739. doi: 10.1136/bmj.f739. No abstract available. PMID 23381588
- Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015 Jan;61:85-117. doi: 10.1016/j.neunet.2014.09.003. Epub 2014 Oct 13. PMID 25462637
- Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sanchez CI. A survey on deep learning in medical image analysis. Med Image Anal. 2017 Dec;42:60-88. doi: 10.1016/j.media.2017.07.005. Epub 2017 Jul 26. PMID 28778026
Identifiers
NCT: NCT07153315 · AU-MEDAI-JOINT-2025-01