A method for the assessment of rheumatoid arthritis using neural network supported static and dynamic thermal analysis

Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by pain, swelling, stiffness, and loss of joint function, making early diagnosis challenging. The study aims to assess the differences between RA patients (n = 70) and healthy individuals (n = 30) while classifying Ritchie Articular Index (RAI) values (0-3) based on inflammation levels using artificial intelligence algorithms. Metacarpophalangeal (MCP), and proximal-interphalangeal (PIP) joints were analyzed for the degree of inflammation. Static thermal data was collected from individuals at rest in a controlled environment. Then, alcohol was applied to the participants' hand regions, followed by a 180-second thermal video recording of the same region. In the pre-processing step, background noise cleaning and alignment were performed. Background was eliminated using Snake algorithm. Thermal video recordings were aligned using Scale Invariant Feature Transform (SIFT) algorithm. The Skeletonization algorithm was employed to detect fingers and joint regions in the images. For static thermal analysis, initial temperature (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{T}_{init}$$\end{document}) values were extracted from the resting thermogram data. In dynamic thermal analysis, the temperature parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{T}_{C}$$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{T}_{R}$$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{\varDelta\:T}_{C}$$\end{document}, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{\varDelta\:T}_{R}$$\end{document} were calculated. A statistical analysis of the four temperature parameters across different RAI values revealed that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{T}_{C}$$\end{document} (p = 0.025) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{T}_{R}$$\end{document} (p = 0.042) exhibited statistically significant differences among the four RAI levels. Machine learning models were trained using the resting temperature values of patient and healthy groups, and the SVM achieved the highest success rate of 93%. It is believed that the proposed system may help diagnose RA in clinical settings and contribute to determining the severity of inflammation.