Arthritis, Rheumatoid (RA)NCT07153315
AI-Based Medical Data Analysis for Differentiating Inflammatory vs Degenerative Joint Diseases in Elderly Patients
An observational study of Arthritis, Rheumatoid (RA), run by Assiut University at 1 site in Egypt.
Trial 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. Read more about this study
- Who is it for?
- Ages 60 years and older
- Conditions the study is for
- Arthritis, Rheumatoid (RA)
- How many people
- 140 participants
- Where
- 1 siteEgypt
- Study dates
- 1 Sep 2025to 1 Oct 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 60 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Egypt1 site
Assiut University Hospital
Asyut, 71515
Mohamed Mahmoud Mohamed, Resident at internal medicine, +20882332278, Mohamed.17289955@med.aun.edu.eg
Prof/soheir Mostafa Kasem, Professor of Internal Medicine
About this study
Official title: Artificial Intelligence-Enhanced Medical Data Analysis for Differentiating Inflammatory and Degenerative Joint Diseases and Detecting of Disease Severity in Elderly Patient
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.
Read the 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.
- Conditions
- Arthritis, Rheumatoid (RA)
- Medical subject headings
- Arthritis, Rheumatoid
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: Within 12 months from baseline assessment.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Sep 2025
- Study start1 Sep 2025 (estimated)
- Primary completion1 Sep 2026 (estimated)
- Study completion1 Oct 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Arthritis, Rheumatoid (RA)Studies of the same condition, open and under way.
- More trials run by Assiut UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Enrolment
- 140 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Assiut University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Mohamed Mahmoud Gamea, Assiut University
- Investigators
- Mohamed Mahmoud Mohamed, Resident at internal medicine, Principal investigator, Assiut University Hospitals - Faculty of Medicine, Assiut University, Egypt
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07153315
- Sponsor's protocol ID
- AU-MEDAI-JOINT-2025-01
- Status last verified
- Aug 2025
- First submitted
- 25 Aug 2025
- Last updated
- 3 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
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.
PubMed 23381588Schmidhuber 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.
PubMed 25462637Litjens 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.
PubMed 28778026
Study at a glance
From the registry record, last updated by the sponsor on 3 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-Enhanced Medical Data Analysis for Differentiating Inflammatory and Degenerative Joint Diseases and Detecting of Disease Severity in Elderly Patient
- ClinicalTrials.gov ID
- NCT07153315
- Acronym / sponsor ID
- AU-MEDAI-JOINT-2025-01
- Conditions
- Arthritis, Rheumatoid (RA)
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 140 participants (estimated)
- Lead sponsor
- Assiut UniversityUniversity, hospital or other
- Study start
- 1 Sep 2025 (estimated)
- Primary completion
- 1 Sep 2026 (estimated)
- Study completion
- 1 Oct 2026 (estimated)
- First posted
- 3 Sep 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Case only
- Time perspective
- Cross-sectional
Who may qualify
- Ages
- 60 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Diagnostic accuracy of AI system
Time frame: 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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Sep 2025.
- Background: PubMed 23381588Bhaumik S. Cardiologists are putting in stents needlessly, doctors say. BMJ. 2013 Feb 4;346:f739. doi: 10.1136/bmj.f739. No abstract available.
- Background: PubMed 25462637Schmidhuber 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.
- Background: PubMed 28778026Litjens 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.
- Related InfoLink provided by the sponsor.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Mohamed Mahmoud Mohamed, Resident at internal medicine principal investigator
Assiut University Hospitals - Faculty of Medicine, Assiut University, Egypt
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Arthritis, Rheumatoid (RA), run by Assiut University at 1 site in Egypt.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 3 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Mohamed Mahmoud Mohamed (+20882332278, Mohamed.17289955@med.aun.edu.eg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic accuracy of AI system (time frame: Within 12 months from baseline assessment.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07153315, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Sep 2025.