AI-Based Medical Data Analysis for Differentiating Inflammatory vs Degenerative Joint Diseases in Elderly Patients

Not yet recruiting

• Conditions: Arthritis, Rheumatoid (RA)

• Registration Number: NCT07153315
• Lead Sponsor: Assiut University

Brief Summary

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.

Study Timeline

• Status Verified: 2025-08
• Study First Submitted: 2025-08-25
• Study First Submitted QC: 2025-08-29
• Last Update Submitted: 2025-08-29
• Study Start: 2025-09-01
• Study First Posted: 2025-09-03
• Last Update Posted: 2025-09-03
• Primary Completion: 2026-09-01
• Study Completion: 2026-10-01

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 140

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Diagnostic accuracy of AI system	Within 12 months from baseline assessment.	Sensitivity, specificity, and AUC of AI algorithm for differentiating inflammatory from degenerative joint diseases, using imaging data, compared to expert rheumatologist diagnosis

Trial Locations

Locations (1)

Assiut University Hospital; 🇪🇬 Asyut, Egypt