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Automated Arthritis Detection Using Artificial Intelligence on Smartphone Photographs

NCT ID: NCT06715488

Last Updated: 2024-12-24

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

ACTIVE_NOT_RECRUITING

Total Enrollment

3000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-11-15

Study Completion Date

2027-12-31

Brief Summary

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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

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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.

Conditions

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Rheumatoid Arthritis &Amp; Other Inflammatory Polyarthropathies Peripheral Spondyloarthritis Inflammatory Arthritis

Keywords

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Artificial intelligence for arthritis diagnosis

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Study Groups

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Inflammatory arthritis

Patients with inflammatory arthritis regardless of etiology including rheumatoid arthritis, psoriatic arthritis, systemic lupus erythematosis and viral arthritis

AI assisted smartphone diagnosis

Intervention Type DIAGNOSTIC_TEST

Patients will examination and clinical photographs for convolutional networks to diagnose inflammatory arthritis

Interventions

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AI assisted smartphone diagnosis

Patients will examination and clinical photographs for convolutional networks to diagnose inflammatory arthritis

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

* Inflammatory arthritis of any etiology

Exclusion Criteria

* Severe deformity that hampers standardization of photographs
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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IISER Pune

UNKNOWN

Sponsor Role collaborator

Med2Measure

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Sanat Phatak, MD, DM

Role: PRINCIPAL_INVESTIGATOR

Med2Measure

Locations

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Rheumatology Clinic

Pune, Maharashtra, India

Site Status

Poona Superspeciality Clinic

Pune, , India

Site Status

Countries

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India

Other Identifiers

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M2M-ID0001

Identifier Type: -

Identifier Source: org_study_id