Predictive Clinical Diagnosis of Rheumatoid Arthritis Flares Using Non-Invasive Infra-red Thermal Imaging and an AI/ML Algorithm

The hypothesis for this clinical research project is that the severity of RA may be detected and predicted using an optimized ML/AI algorithm that uses infrared thermal images of inflamed joints and standard clinical RA-related markers (i.e., ESR and CRP) by computing DAS-28 ESR scores in real-time. The infrared thermal images coupled with clinical laboratory markers and the ML/AI algorithm are expected to assist a practicing clinician in the RA diagnosis and the prediction of the occurrence of flares in RA patients. Physicians who use this technology, would need minimum training and will be able to accurately and reliably diagnose RA using a cheaper method which does not involve incident radiation emitted by other imaging modalities such a X-RAY, musculoskeletal (MSK) ultrasound, or a magnetic resonance imaging (MRI). The aim would be to have the Infrared thermal imaging devices at remote VA clinics that do not have a rheumatology specialist where veterans can go for their inflammatory arthritis flare and get this image by the local VA RN. These clinical results can then be assessed by and discussed with a Rheumatologist via telehealth visits.