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    A comparison of thermal characteristics of the small joints of the hands between patients with rheumatoid arthritis and healthy controls
    (Turkish League Against Rheumatism, 2024) Ugur, Sevcan; Irim, Yakup; Yuecel, Ayse Ayca; Carlak, Hamza Feza; Kacar, Cahit
    Objectives: This study aims to investigate the thermal characteristics of the small joints of the hands between patients with rheumatoid arthritis (RA) and healthy controls. Patients and methods: Between December 2020 and May 2021, a total of 52 RA patients (9 males, 43 females; mean age: 52.1 +/- 11.1 years; range, 38 to 68 years) who met the revised American College of Rheumatology and European League Against Rheumatism (ACR/EULAR) classification criteria and 26 healthy controls (10 males, 16 females; mean age: 51.2 +/- 8.2 years; range, 38 to 68 years) were included. Joint tenderness was evaluated using Ritchie articular index (RAI). Joint tenderness was scored from 0 to 3. Thermal data were collected from the hand regions of individuals. A FLIR T450sc microbolometer infrared thermal camera with 320 & YEN;240 resolution was used for the thermography of individuals. Bilaterally proximal interphalangeal joints (1-5) and metacarpophalangeal joints (1-5) were evaluated. The mean temperature was compared between the patients and healthy controls. Results: The mean disease duration of patients with RA was 10.4 +/- 8.9 years. The mean temperature values of thejoints in the patients with a RA RAI score of 0, 1, 2, 3 were 32.43 +/- 1.59 degrees C; 32.71 +/- 1.36 degrees C; 33.12 +/- 1.23 degrees C; 33.60 +/- 0.99 degrees C, respectively. The mean temperature was 31.14 +/- 1.51 degrees C in healthy controls. The mean temperature values of the joints in the RA patients with RAI score of 0 was higher compared to healthy controls (p<0.05). Patients with a Ritchie sensitivity score of 1 had a higher mean temperature compared to patients with score of 0 (p<0.05). In RA patients, thejoints with a RAI score of 1 had higher mean temperature values than thejoints with RAI score of 0 (p<0.05). The mean temperature values of the joints with RAI score of 2 were also higher than the joints with RAI score of 1 (p<0.05). Conclusion: Our study results suggest that thermal imaging may be an objective tool for diagnosis and assessing disease activity in RA.
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    Öğe
    A method for the assessment of rheumatoid arthritis using neural network supported static and dynamic thermal analysis
    (Springer, 2026) Carlak, H. Feza; Irim, Yakup; Ugur, Sevcan; Kacar, Cahit; Yucel, Ayse Ayca
    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.