Inflammatory Arthritis, Peripheral Spondyloarthritis, Rheumatoid Arthritis &Amp; Other Inflammatory Polyarthropathies
Conditions
Keywords
Artificial intelligence for arthritis diagnosis
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.
Interventions
DIAGNOSTIC_TESTAI assisted smartphone diagnosis
Patients will examination and clinical photographs for convolutional networks to diagnose inflammatory arthritis
Sponsors
IISER Pune
Med2Measure
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Inflammatory arthritis of any etiology
Exclusion criteria
* Severe deformity that hampers standardization of photographs
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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 |
Countries
India
Outcome results
None listed