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

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dc.contributor.authorCarlak, H. Feza
dc.contributor.authorIrim, Yakup
dc.contributor.authorUgur, Sevcan
dc.contributor.authorKacar, Cahit
dc.contributor.authorYucel, Ayse Ayca
dc.date.accessioned2026-04-25T14:19:54Z
dc.date.available2026-04-25T14:19:54Z
dc.date.issued2026
dc.departmentSinop Üniversitesi
dc.description.abstractRheumatoid 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.
dc.identifier.doi10.1007/s13246-026-01734-2
dc.identifier.issn2662-4729
dc.identifier.issn2662-4737
dc.identifier.pmid41910922
dc.identifier.scopus2-s2.0-105034760888
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s13246-026-01734-2
dc.identifier.urihttps://hdl.handle.net/11486/8231
dc.identifier.wosWOS:001728355000001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofPhysical and Engineering Sciences in Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260420
dc.subjectRheumatoid arthritis
dc.subjectThermal imaging
dc.subjectMedical imaging
dc.subjectRitchie articular index
dc.subjectMachine learning
dc.titleA method for the assessment of rheumatoid arthritis using neural network supported static and dynamic thermal analysis
dc.typeArticle

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