Harnessing Deep Learning for Rheumatoid Arthritis Detection: Advancements in Automated X-Ray Analysis

U. Shirly Victoria, N. Ranjith

Abstract: Rheumatoid arthritis (RA) is an autoimmune chronic disease characterized by persistent inflammation, pain and progressive structural damage of the joints, causing the cartilage to become affected, and may progress into deformities in the joints and loss of function. In addition to involvement of synovial joints, RA may involve extra-articular other systems such as cardiovascular system, pulmonary system and may lead to greater morbidity. Imaging techniques, such X-ray, Magnetic Resonance Imaging (MRI), ultrasound (US), bone scintigraphy, and Positron Emission Tomography (PET), are critical in the identification, monitoring, and assessment of disease progression. Among them, X-ray is commonly utilized in the assessment of structural damage, and are also more sensitive to detecting initial inflammatory changes like synovitis and bone erosions. However, traditional imaging analysis is usually tedious, subjective, and prone to inter- as well as intra-observer change, particularly in the early stages of the disease. To overcome these limitations, the Machine Learning (ML) strategies were used in order to help identify and categorize RA using automated methods through the use of features of joint space contraction, texture patterns, and erosion features that are extracted on the basis of medical images. Although the efficacy of ML methods, depends on manual feature engineering that can restrict scalability and decrease generalization to diverse datasets. Deep Learning (DL) methods, particularly Convolutional Neural Networks (CNNs), has recently emerged as a formidable force capable of autonomously learning in the hierarchical depiction of feature representations derived of raw imaging data. These are models with better performance in identifying subtle pathological changes, and allows a more accurate and efficient diagnosis of RA and its classification into severities. This paper reviews and discusses the DL-approaches to detecting RA using X-ray images, their methodologies, benefits, shortcomings, and sheds some light on developing reliable and practically usable diagnostic systems.

Keywords: Rheumatoid arthritis, Deep Learning, X-ray, Classification, Segmentation

How to Cite?: U. Shirly Victoria, N. Ranjith, "Harnessing Deep Learning for Rheumatoid Arthritis Detection: Advancements in Automated X-Ray Analysis", Volume 15 Issue 7, July 2026, International Journal of Science and Research (IJSR), Pages: 1422-1430, https://www.ijsr.net/getabstract.php?paperid=SR26716130315, DOI: https://dx.doi.org/10.21275/SR26716130315

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