Framework for Rheumatoid Arthritis Assessment Using Thermal Images Based on DnCNN-MLR Hybrid Algorithm and Joint Temperature Indexing

Background: Rheumatoid arthritis (RA) is a slow progressive autoimmune disease. RA disproportionately affects women due to hormonal and immune variations. During pregnancy, hormonal and immune system changes vary drastically and may lead to RA. Traditional diagnostic techniques are blood biomarkers and clinical assessments. The above methods fail to detect RA at earlier stage due to subclinical inflammation and pregnancy-related physiological changes. Thermal imaging, as a non-invasive and radiation-free approach, can reveal temperature asymmetries across inflamed joints, offering a safer diagnostic pathway for pregnant women. Objective: In this paper, non-invasive RA detection is performed using finger, leg and hand thermal images. A pregnancy-aware rheumatoid arthritis (PARA) diagnostic framework is proposed. The PARA framework uses hybrid deep learning algorithms to classify RA inflammation states, such as normal, moderate and high. Methods: Using the PARA framework, thermal images were obtained from pregnant women. A total of 28 major bone joints were captured across four physiological states, including normal and before pregnancy. The thermal images were obtained from normal women, pregnant women, and women after pregnancy using a smartphone -based high-resolution USB thermal camera. Preprocessing was performed using bilateral, Non-Local Means (NLM), and guided filters to enhance thermal images for clarity. The guided filter preserves the edges and suppresses noise. Our proposed Denoising Convolutional Neural Network (DnCNN) algorithm was applied to preprocessed images to extract inflammation-sensitive thermal features. Finally, Multiple Linear Regression (MLR) was employed to predict the inflammation scale using the statistical values from the DnCNN-processed images. Results: The regression analysis revealed a strong correlation between thermal gradients and inflammatory severity across elbow, hand, and knee joints; i.e., the K-fold accuracy was 93.84 ± 0.71. The Modified Clinical Discord Activity Index (MCDAI) categorizes inflammation as low, moderate, and high, and these values were used in the PARA framework for inflammation level prediction supporting early clinical decision-making. Conclusion: The proposed PARA framework has high diagnostic potential to classify RA stages in pregnant women through a non-invasive and pregnancy-specific assessment. The PARA framework reduces dependency on laboratory tests and supports timely therapeutic interventions.