Automated Arthritis Detection Using Artificial Intelligence on Smartphone Photographs

Active, not recruiting

• Conditions: Rheumatoid Arthritis &Amp; Other Inflammatory Polyarthropathies; Peripheral Spondyloarthritis; Inflammatory Arthritis

• Registration Number: NCT06715488
• Lead Sponsor: Med2Measure

Brief Summary

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

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 Timeline

• Study Start: 2024-11-15
• Study First Submitted: 2024-11-28
• Study First Submitted QC: 2024-11-28
• Status Verified: 2024-12
• Study First Posted: 2024-12-04
• Last Update Submitted: 2024-12-19
• Last Update Posted: 2024-12-24
• Primary Completion: 2027-12
• Study Completion: 2027-12

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 3000

Inclusion Criteria

• Inflammatory arthritis of any etiology

Exclusion Criteria

• Severe deformity that hampers standardization of photographs

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Accuracy of AI diagnosis against specialist (rheumatologist) opinion	3 years	Concordance of detection of synovitis by convolutional neural network (binary) with a clinically diagnosed specialist opinion (rheumatologist opinion)

Secondary Outcome Measures

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

Trial Locations

Locations (2)

Rheumatology Clinic; 🇮🇳 Pune, Maharashtra, India

Poona Superspeciality Clinic; 🇮🇳 Pune, India