Automated Arthritis Detection Using Artificial Intelligence on Smartphone Photographs (AISynovitis)

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Inflammatory Arthritis; Peripheral Spondyloarthritis; Rheumatoid Arthritis &Amp; Other Inflammatory Polyarthropathies
• Intervention / Treatment: Diagnostic test: AI assisted smartphone diagnosis

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

1. Collection of photos in a standardized manner using custom designed boxes
2. Using and testing a browser pipeline
3. 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.
4. 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 Type: Observational
• Enrollment (Estimated): 3000

Contacts and Locations

Study Locations

• India
  • Pune, India, 411001
    • Poona Superspeciality Clinic
  • Maharashtra
    • Pune, Maharashtra, India, 411004
      • Rheumatology Clinic

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

All patients with inflammatory arthritis. This can include but is not limited to rheumatoid arthritis, lupus, psoriatic arthritis, peripheral spondyloarthritis, viral arthritis.

Description

Inclusion Criteria:

• Inflammatory arthritis of any etiology

Exclusion Criteria:

• Severe deformity that hampers standardization of photographs

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Inflammatory arthritis; Patients with inflammatory arthritis regardless of etiology including rheumatoid arthritis, psoriatic arthritis, systemic lupus erythematosis and viral arthritis	Diagnostic test: AI assisted smartphone diagnosis; Patients will examination and clinical photographs for convolutional networks to diagnose inflammatory arthritis

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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)	3 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy of AI diagnosis against imaging diagnosis on Ultrasound	Concordance of detection of synovitis by convolutional neural network (binary) compared to musculoskeletal ultrasound	3 years
Sensitivity to change	Can the convolutional neural network detect change from an inflamed to an non-inflamed joint	3 years

Collaborators and Investigators

• Sponsor: Med2Measure
• Collaborators: IISER Pune
• Investigators: Principal Investigator: Sanat Phatak, MD, DM, Med2Measure

Study record dates

Study Major Dates

• Study Start (Actual): November 15, 2024
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2027

Study Registration Dates

• First Submitted: November 28, 2024
• First Submitted That Met QC Criteria: November 28, 2024
• First Posted (Actual): December 4, 2024

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: December 19, 2024
• Last Verified: December 1, 2024

More Information

Terms related to this study

• Keywords: Artificial intelligence for arthritis diagnosis
• Additional Relevant MeSH Terms: Bone Diseases; Musculoskeletal Diseases; Joint Diseases; Infections; Bone Diseases, Infectious; Spinal Diseases; Arthritis; Spondylitis; Spondylarthritis
• Other Study ID Numbers: M2M-ID0001

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Deidentified photographs and clinical information excluding HIPAA variables
• IPD Sharing Time Frame: January 2027-January 2028
• IPD Sharing Access Criteria: Researchers with a scientific plan can get in touch with the principal investigator with a brief note.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; ICF; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No