Rheumatoid Arthritis &Amp; Other Inflammatory PolyarthropathiesAISynovitisNCT06715488
Automated Arthritis Detection Using Artificial Intelligence on Smartphone Photographs
An observational study of Rheumatoid Arthritis &Amp; Other Inflammatory Polyarthropathies and Peripheral Spondyloarthritis, run by Med2Measure at 2 sites in India.
Trial summary
The investigators are testing the ability of convolutional neural networks (CNNs), that is artificial intelligence, on smartphone photographs in detecting inflammatory arthritis. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Rheumatoid Arthritis &Amp; Other Inflammatory Polyarthropathies, Peripheral Spondyloarthritis, Inflammatory Arthritis
- What is being tested?
- AI assisted smartphone diagnosis
- How many people
- 3,000 participants
- Where
- 2 sitesIndia
- Study dates
- 15 Nov 2024to Dec 2027
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: All patients with inflammatory arthritis. This can include but is not limited to rheumatoid arthritis, lupus, psoriatic arthritis, peripheral spondyloarthritis, viral arthritis.
Who may be able to join
- Inflammatory arthritis of any etiology
Who cannot join
- Severe deformity that hampers standardization of photographs
Trial locations and contacts
2 sites in 1 country.
India2 sites
Rheumatology Clinic
Pune, Maharashtra, 411004
Poona Superspeciality Clinic
Pune, 411001
About this study
Official title: Automated Detection Methods for Inflammatory Arthritis and Formation of an Image Database
The investigators are testing the ability of convolutional neural networks (CNNs), that is artificial intelligence, on smartphone photographs in detecting inflammatory arthritis. This promises to be an efficient, accurate, and non-invasive diagnostic tool that will significantly improve early detection and management of inflammatory arthritis.
Read the detailed description
Over the past 4 years the investigators have aimed to help the early detection of arthritis leveraging artificial intelligence. This project aims to detect arthritis based on smart phone photographs of joint areas that make it scalable and available in the community. This group first developed a compelling proof-of-concept pipeline and models using 100 patients. (published in Frontiers in Medicine, Nov 2023, wherein they demonstrated that this technology works with reasonable accuracy in the lab, viz Technology Readiness Level currently stands at 3-4). They followed with a newer paper (submitted for publication, available on preprint server MedRxiv) that trained two different CNNs, a screening CNN on uncropped hands that distinguishes patients from controls followed by joint specific detections.
The system involves supporting infrastructure that will enable efficient detection of arthritis. This includes
- Collection of photos in a standardized manner using custom designed boxes
- Using and testing a browser pipeline
- The CNN models will be trained on the dataset of photographs taken in this and results will be deployed to doctors in the community. This ensures a doctor in the loop that can later take action on the results for further confirmatory tests or management.
- Understanding knowledge, attitude of patients and doctors towards AI in clinical decision making algorithms
This is a Prospective, non-interventional study and this project only involves an investigator taking a smartphone photograph of some joint areas kept in standardized positions. This involves no risk to the patient.
- Conditions
- Rheumatoid Arthritis &Amp; Other Inflammatory Polyarthropathies, Peripheral Spondyloarthritis, Inflammatory Arthritis
- Being tested
- AI assisted smartphone diagnosis
- Keywords
- Artificial intelligence for arthritis diagnosis
- Medical subject headings
- Arthritis, Rheumatoid, Arthritis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI assisted smartphone diagnosisDiagnostic test
Patients will examination and clinical photographs for convolutional networks to diagnose inflammatory arthritis
| Study group | Role | What they receive |
|---|---|---|
| Inflammatory arthritis | Patients with inflammatory arthritis regardless of etiology including rheumatoid arthritis, psoriatic arthritis, systemic lupus erythematosis and viral arthritis Receives: Diagnostic Test: AI assisted smartphone diagnosis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of AI diagnosis against specialist (rheumatologist) opinion
Concordance of detection of synovitis by convolutional neural network (binary) with a clinically diagnosed specialist opinion (rheumatologist opinion)
Measured: 3 years
Secondary outcomes (2)
Accuracy of AI diagnosis against imaging diagnosis on Ultrasound
Concordance of detection of synovitis by convolutional neural network (binary) compared to musculoskeletal ultrasound
Measured: 3 years
Sensitivity to change
Can the convolutional neural network detect change from an inflamed to an non-inflamed joint
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Dec 2024
- Study start15 Nov 2024
- Primary completionDec 2027 (estimated)
- Study completionDec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Rheumatoid Arthritis &Amp; Other Inflammatory PolyarthropathiesStudies of the same condition, open and under way.
- 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-control
- Time perspective
- Prospective
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Med2Measure
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- IISER Pune
- Responsible party
- Sponsor
- Investigators
- Sanat Phatak, MD, DM, Principal investigator, Med2Measure
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06715488
- Sponsor's protocol ID
- M2M-ID0001
- Acronym
- AISynovitis
- Status last verified
- Dec 2024
- First submitted
- 28 Nov 2024
- Last updated
- 24 Dec 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Deidentified photographs and clinical information excluding HIPAA variables
Publications
Saptarshi R, Attarde D, Shah A, Deshpande H, Joshi S, Karanjkar A, Kakade G, Ekbote G, Zanwar A, Goel P, Phatak S. Detecting inflammatory arthritis in hand smartphone photographs: development and validation of a computer vision model in clinical settings. Rheumatology (Oxford). 2026 Jun 3;65(6):keag287. doi: 10.1093/rheumatology/keag287.
PubMed 42226010
Study at a glance
From the registry record, last updated by the sponsor on 24 Dec 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Automated Detection Methods for Inflammatory Arthritis and Formation of an Image Database
- ClinicalTrials.gov ID
- NCT06715488
- Acronym / sponsor ID
- AISynovitis / M2M-ID0001
- Conditions
- Rheumatoid Arthritis &Amp; Other Inflammatory Polyarthropathies, Peripheral Spondyloarthritis, Inflammatory Arthritis
- Keywords
- Artificial intelligence for arthritis diagnosis
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Med2MeasureIndustry (pharma, biotech, devices)
- Collaborators
- IISER Pune
- Study start
- 15 Nov 2024 (actual)
- Primary completion
- Dec 2027 (estimated)
- Study completion
- Dec 2027 (estimated)
- First posted
- 4 Dec 2024 (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.
The investigators are testing the ability of convolutional neural networks (CNNs), that is artificial intelligence, on smartphone photographs in detecting inflammatory arthritis. This promises to be an efficient, accurate, and non-invasive diagnostic tool that will significantly improve early detection and management of inflammatory arthritis.
Detailed description
Over the past 4 years the investigators have aimed to help the early detection of arthritis leveraging artificial intelligence. This project aims to detect arthritis based on smart phone photographs of joint areas that make it scalable and available in the community. This group first developed a compelling proof-of-concept pipeline and models using 100 patients. (published in Frontiers in Medicine, Nov 2023, wherein they demonstrated that this technology works with reasonable accuracy in the lab, viz Technology Readiness Level currently stands at 3-4). They followed with a newer paper (submitted for publication, available on preprint server MedRxiv) that trained two different CNNs, a screening CNN on uncropped hands that distinguishes patients from controls followed by joint specific detections.
The system involves supporting infrastructure that will enable efficient detection of arthritis. This includes
- Collection of photos in a standardized manner using custom designed boxes
- Using and testing a browser pipeline
- The CNN models will be trained on the dataset of photographs taken in this and results will be deployed to doctors in the community. This ensures a doctor in the loop that can later take action on the results for further confirmatory tests or management.
- Understanding knowledge, attitude of patients and doctors towards AI in clinical decision making algorithms
This is a Prospective, non-interventional study and this project only involves an investigator taking a smartphone photograph of some joint areas kept in standardized positions. This involves no risk to the patient.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Inflammatory arthritis | – | Diagnostic Test: AI assisted smartphone diagnosisPatients with inflammatory arthritis regardless of etiology including rheumatoid arthritis, psoriatic arthritis, systemic lupus erythematosis and viral arthritis |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- All patients with inflammatory arthritis. This can include but is not limited to rheumatoid arthritis, lupus, psoriatic arthritis, peripheral spondyloarthritis, viral arthritis.
Key inclusion criteria (1 listed)
- Inflammatory arthritis of any etiology
Key exclusion criteria (1 listed)
- Severe deformity that hampers standardization of photographs
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI assisted smartphone diagnosisPatients will examination and clinical photographs for convolutional networks to diagnose inflammatory arthritis | Diagnostic test | Inflammatory arthritis |
Primary outcome
Accuracy of AI diagnosis against specialist (rheumatologist) opinion
Time frame: 3 years
Concordance of detection of synovitis by convolutional neural network (binary) with a clinically diagnosed specialist opinion (rheumatologist opinion)
2 secondary outcomes
Accuracy of AI diagnosis against imaging diagnosis on Ultrasound
Time frame: 3 years
Sensitivity to change
Time frame: 3 years
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 24 Dec 2024.
- Derived from this study: PubMed 42226010Saptarshi R, Attarde D, Shah A, Deshpande H, Joshi S, Karanjkar A, Kakade G, Ekbote G, Zanwar A, Goel P, Phatak S. Detecting inflammatory arthritis in hand smartphone photographs: development and validation of a computer vision model in clinical settings. Rheumatology (Oxford). 2026 Jun 3;65(6):keag287. doi: 10.1093/rheumatology/keag287.
- Individual participant dataSharing planned: Yes. Deidentified photographs and clinical information excluding HIPAA variables
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. This study is not recruiting at the moment.
Sanat Phatak, MD, DM principal investigator
Med2Measure
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Rheumatoid Arthritis &Amp; Other Inflammatory Polyarthropathies and Peripheral Spondyloarthritis, run by Med2Measure at 2 sites in India.
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 1 inclusion and 1 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: under way, not looking for participants, as of the sponsor's last update on 24 Dec 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in India. 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of AI diagnosis against specialist (rheumatologist) opinion (time frame: 3 years). 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 NCT06715488, which also lists 1 linked publication.
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 24 Dec 2024.