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.
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
3,000
Patients will examination and clinical photographs for convolutional networks to diagnose inflammatory arthritis
Rheumatology Clinic
Pune, Maharashtra, India
Poona Superspeciality Clinic
Pune, India
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)
Time frame: 3 years
Accuracy of AI diagnosis against imaging diagnosis on Ultrasound
Concordance of detection of synovitis by convolutional neural network (binary) compared to musculoskeletal ultrasound
Time frame: 3 years
Sensitivity to change
Can the convolutional neural network detect change from an inflamed to an non-inflamed joint
Time frame: 3 years
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