Deep Learning and Quantum Computing Based Evaluation of Rheumatoid Arthritis Using Hand X Ray and Thermal Images

Abstract

Rheumatoid arthritis (RA) is a prolonged chronic autoimmune illness that primarily impacts the small joints. The immune system of the body inadvertently attacks its tissues, particularly the synovium, and a thin membrane that surrounds the joints, which leads to the development of RA. RA causes joint erosions, which are visible at the margins of the joint, joint space narrowing (JSN) that narrows the space between joints, and soft tissue inflammation around the affected joints. The symptoms of RA include stiffness, swelling, and joint discomfort, particularly in the morning or after periods of inactivity. RA can be identified using a combination of physiological examination, diagnostic test modalities, and medical history. The specific antibodies and disease-related inflammatory indicators can be evaluated using various blood test such as erythrocyte sedimentation rate (ESR), C- reactive protein (CRP), rheumatoid factor (RF), and anti-cyclic citrullinated peptide antibody (anti-CCP). The various diagnostic testing modalities include radiography, ultrasonography, computed tomography (CT), magnetic resonance imaging (MRI), and thermography. Magnetic resonance imaging (MRI), computed tomography (CT), radiography, and ultrasound are among the diagnostic modalities used to evaluate RA. These methods do, however, have certain restrictions. CT images provide only the bone structure and detect bone erosion at an early stage, but it finds difficult to assess the characteristics of synovial tissue However, synovial tissue properties are more challenging to evaluate. MRI is a costly and time-consuming test, but it can identify synovitis and bone deterioration newline