Clinical trial NCT06715488: Automated Arthritis Detection Using Artificial Intelligence on Smartphone Photographs
Official title: Automated Detection Methods for Inflammatory Arthritis and Formation of an Image Database
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Rheumatoid Arthritis &Amp; Other Inflammatory Polyarthropathies
Peripheral Spondyloarthritis
Inflammatory Arthritis
Keywords: Artificial intelligence for arthritis diagnosis
Lead sponsor: Med2Measure
Sponsor class: INDUSTRY
Collaborators: name: IISER Pune
First posted: 2024-12-04
Enrollment: 3000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
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.

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.

Eligibility criteria: Inclusion Criteria:

* Inflammatory arthritis of any etiology

Exclusion Criteria:

* Severe deformity that hampers standardization of photographs

Primary outcomes:
measure: Accuracy of AI diagnosis against specialist (rheumatologist) opinion
timeFrame: 3 years
Secondary outcomes:
measure: Accuracy of AI diagnosis against imaging diagnosis on Ultrasound
timeFrame: 3 years
measure: Sensitivity to change
timeFrame: 3 years
Other outcomes:
Locations:
city: Pune
country: India
facility: Rheumatology Clinic
state: Maharashtra
city: Pune
country: India
facility: Poona Superspeciality Clinic
Study officials:
affiliation: Med2Measure
name: Sanat Phatak, MD, DM
role: PRINCIPAL_INVESTIGATOR
References: