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

NCT07153315 | Not Yet Recruiting

• 1 other identifier: AU-MEDAI-JOINT-2025-01
• Study Type: observational
• Target: 140
• Locations: 1 country
• Sites: 1

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.

Conditions

Arthritis, Rheumatoid (RA)

Outcome Measures

Primary Outcomes (1)

• Diagnostic accuracy of AI system

  Sensitivity, specificity, and AUC of AI algorithm for differentiating inflammatory from degenerative joint diseases, using imaging data, compared to expert rheumatologist diagnosis

  Within 12 months from baseline assessment.

Eligibility Criteria

• Age: 60 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Elderly patients (≥60 years) presenting to Assiut University Hospitals with clinical suspicion or diagnosis of musculoskeletal disorders, specifically inflammatory joint diseases (such as rheumatoid arthritis) or degenerative joint diseases (such as osteoarthritis), and who have relevant imaging studies (X-ray, MRI, or ultrasound) available for analysis.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Assiut University: lead

Study Sites (1)

Assiut University Hospital

Asyut, 71515, Egypt

Location

Related Publications (3)

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

Related Links

• Related Info

MeSH Terms

Conditions

Arthritis, Rheumatoid

Condition Hierarchy (Ancestors)

Arthritis; Joint Diseases; Musculoskeletal Diseases; Rheumatic Diseases; Connective Tissue Diseases; Skin and Connective Tissue Diseases; Autoimmune Diseases; Immune System Diseases

Study Officials

• Mohamed Mahmoud Mohamed, Resident at internal medicine

  Assiut University Hospitals - Faculty of Medicine, Assiut University, Egypt

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Mohamed Mahmoud Mohamed

CONTACT

+20882332278; Mohamed.17289955@med.aun.edu.eg

Prof/soheir Mostafa Kasem, Professor of Internal Medicine

CONTACT

+201069347314; Soheir@aun.edu.eg

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principle investigator

Study Record Dates

• First Submitted: August 25, 2025
• First Posted: September 3, 2025
• Study Start: September 1, 2025
• Primary Completion (Estimated): September 1, 2026
• Study Completion (Estimated): October 1, 2026
• Last Updated: September 3, 2025

Record last verified: 2025-08

Locations

EG (1)