Comparing the heating rate of the proximal phalanx of the fingers in rheumatoid arthritis and healthy subjects

• Autorzy: Ihnatouski MMikhail , Pauk Jolanta , Daunoraviciene Kristina , Ziziene Jurgita

Identyfikatory

DOI

10.2478/ama-2024-0052

• Języki publikacji: EN

Abstrakty

EN

Thermography is a non-invasive imaging technique that has been used for the assessment of rheumatoid arthritis (RA). The purpose of this research was to compare the heating rate of the proximal phalanx of the fingers and the whole palms in RA and that of healthy subjects. The study was conducted on 48 patients with high disease activity, hospitalised for RA, and 45 healthy subjects. The thermograms were taken with the FLIR camera E60bx. Subjects were instructed to immerse both hands up to the wrist in water thermostatically controlled at 0°C for 30 s. Then, the hands were pulled out of the water; the warm-up period was 180 s. Image pre-processing included: segmentation, extraction and anatomy identification. The mean value of the heating rate for whole palms and the proximal phalanx of the fingers in the RA group was lower than that in the control group (p < 0.05). This coincides with the uneven flow of the heat-transfer blood caused by the disease. However, the difference between the heating rates of the proximal phalanx of the fingers was greater than that of the entire hand. In addition, the proximal phalanx heating rates of the second, third and fourth fingers were higher than those of the outermost two fingers. The study may be used to develop clinical tools in the detection of abnormal heat signatures in the phalanx proximal of the fingers.

Słowa kluczowe

EN

active thermography; rheumatoid arthritis; proximal phalanx; image processing

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Acta Mechanica et Automatica
• Rocznik: 2024
• Tom: Vol. 18, no. 3
• Strony: 490--495
• Opis fizyczny: Bibliogr. 29 poz., rys., tab., wykr.

Twórcy

autor

Ihnatouski MMikhail

• Scientific and Research Department, Yanka Kupala State University of Grodno, Elizy Azeska 22, 230023 Grodno, Belarus

• https://orcid.org/0000-0003-4350-3401

autor

Pauk Jolanta

• Mechanical Engineering Department, Bialystok University of Technology, Wiejska 45C, 15-351 Bialystok, Poland

• https://orcid.org/0000-0001-8607-7129

autor

Daunoraviciene Kristina

• Faculty, Department, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius, Lithuania

• https://orcid.org/0000-0003-0898-4860

autor

Ziziene Jurgita

• Faculty, Department, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius, Lithuania

• https://orcid.org/0000-0002-2624-4375

Bibliografia

• 1. McInnes IB, Schett G. The pathogenesis of rheumatoid arthritis. N Engl J Med. 2011;365:2205–19. https://doi.org/10.1056/NEJMra1004965
• 2. Branco JHL, Branco RLL, Siqueira TC, de Souza LC, Dalago KMS, Andrade A. Clinical applicability of infrared thermography in rheu-matic diseases: A systematic review. J Therm Biol. 2022; 104:103172. https://doi.org/10.1016/j.jtherbio.2021.103172
• 3. Sanchez BM, Lesch M, Brammer D, Bove SE, Thiel M, Kilgore KS. Use of a portable thermal imaging unit as a rapid, quantitative meth-od of evaluating inflammation and experimental arthritis. J Pharmacol Toxicol Methods. 2008;57(3):169-75. https://doi.org/10.1016/j.vascn.2008.01.003
• 4. Kow J, Tan YK. An update on thermal imaging in rheumatoid arthritis. Joint Bone Spine. 2023;90(3):105496. https://doi.org/10.1016/j.jbspin.2022.105496
• 5. Pauk J, Wasilewska A., Ihnatouski M. Infrared thermography sensor for disease activity detection in rheumatoid arthritis patients. Sensors. 2019;19(16):3444. https://doi.org/10.3390/s19163444
• 6. Pauk J, Ihnatouski M, Wasilewska A. Detection of inflammation from finger temperature profile in rheumatoid arthritis. Med Biol Eng Com-put. 2019;57(12):2629-2639. https://doi.org/10.1007/s11517-019-02055-1.
• 7. Morales-Ivorra I, Narváez J, Gómez-Vaquero C, Moragues C, Nolla JM, Narváez JA, Marín-López MA. A Thermographic Disease Activity Index for remote assessment of rheumatoid arthritis. RMD Open. 2022;8(2):e002615. https://doi.org/10.1136/rmdopen-2022-002615
• 8. Morales-Ivorra I, Narváez J, Gómez-Vaquero C, Moragues C, Nolla JM, Narváez JA, Marín-López MA. Assessment of inflammation in patients with rheumatoid arthritis using thermography and machine learning: a fast and automated technique. RMD Open. 2022;8(2): e002458. https://doi.org/10.1136/rmdopen-2022-002458
• 9. Bardhan S, Bhowmik MK. 2-Stage classification of knee joint ther-mograms for rheumatoid arthritis prediction in subclinical inflamma-tion. Australas Phys Eng Sci Med. 2019;42(1):259-277. https://doi.org/10.1007/s13246-019-00726-9
• 10. Ahalya RK, Snekhalatha U, Dhanraj VJ. Automated segmentation and classification of hand thermal images in rheumatoid arthritis us-ing machine learning algorithms: A comparison with quantum ma-chine learning technique. Therm Biol. 2023;111:103404. https://doi.org/10.1016/j.jtherbio.2022.103404
• 11. Snekhalatha U, Anburajan M, Sowmiya V, Venkatraman B, Menaka M: Automated hand thermal image segmentation and feature extrac-tion in the evaluation of rheumatoid arthritis, Proc Inst Mech Eng H 2015;229(4):319-31. https://doi.org/10.1177/0954411915580809
• 12. Tripoliti EE, Fotiadis D, Argyropoulou M. Automated segmentation and quantification of inflammatory tissue of the hand in rheumatoid arthritis patients using magnetic resonance imaging data. Artif Intell Med 2007;40(2):65-85. https://doi.org/10.1016/j.artmed.2007.02.003
• 13. Venerito V, Angelini O, Cazzato G, Lopalco G, Maiorano E, Cimmino A, et al. A convolutional neural network with transfer learning for au-tomatic discrimination between low and high-grade synovitis: a pilot study. Intern Emerg Med. 2021;16:1457–65. https://doi.org/10.1007/s11739-020-02583-x
• 14. Folle L, Meinderink T, Simon D, Liphardt AM, Krönke G, et al. Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density. Sci Rep. 2021; 11:9697–706. https://doi.org/10.1038/s41598-021-89111-9
• 15. Norgeot B, Glicksberg BS, Trupin L, Lituiev D, Gianfrancesco M, Oskotsky B, et al. Assessment of a deep learning model based on electronic health record data to forecast clinical outcomes in patients with rheumatoid arthritis. JAMA Netw Open. 2019;2:e190606. https://doi.org/10.1001/jamanetworkopen.2019.0606
• 16. Fukae J, Isobe M, Hattori T, Fujieda Y, Kono M, Abe N, et al. Convo-lutional neural network for classification of two-dimensional array im-ages generated from clinical information may support diagnosis of rheumatoid arthritis. Sci Rep. 2020;10:5648. https://doi.org/10.1038/s41598-020-62634-3
• 17. Üreten K, Erbay H, Maraş HH. Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network. Clin Rheu-matol. 2020;39:969–74. https://doi.org/10.1007/s10067-019-04487-4
• 18. Christensen ABH, Just SA, Andersen JKH, Savarimuthu TR. Apply-ing cascaded convolutional neural network design further enhances automatic scoring of arthritis disease activity on ultrasound images from rheumatoid arthritis patients. Ann Rheum Dis. 2020;79:1189–93. https://doi.org/10.1136/annrheumdis-2019-216636
• 19. Tan YK, Hong C, Li H, Allen JC Jr, Thumboo J. Thermography in rheumatoid arthritis: a comparison with ultrasonography and clinical joint assessment. Clin Radiol. 2020;75(12):963.e17-963.e22. https://doi.org/10.1016/j.crad.2020.08.017
• 20. Umapathy S, Thulasi R, Gupta N, Sivanadhan S. Thermography and colour Doppler ultrasound: a potential complementary diagnostic tool in evaluation of rheumatoid arthritis in the knee region. Biomed Tech (Berl) 2020;26;65(3):289-299. https://doi.org/10.1515/bmt-2019-0051
• 21. Mountz JM, Alavi A, Mountz JD. Emerging optical and nuclear medi-cine imaging methods in rheumatoid arthritis. Nat Rev Rheumatol. 2012;8(12):719-28. https://doi.org/10.1038/nrrheum.2012.148
• 22. Tan YK, Hong C, Li H, Allen JC Jr, Thumboo J. A novel use of combined thermal and ultrasound imaging in detecting joint inflam-mation in rheumatoid arthritis. Eur J Radiol. 2021;134:109421. https://doi.org/10.1016/j.ejrad.2020.109421
• 23. Dreher R, Müller K, Grebe SF, Altaras J, Federlin K. [Scintigraphic, thermographic and radiographic findings in rheumatoid arthritis (RA) and their value for diagnosis and therapy]. Verh Dtsch Ges Inn Med. 1978;(84):1492-6.
• 24. Tegelberg A, Kopp S. Skin surface temperature over the temporo-mandibular and metacarpophalangeal joints in individuals with rheumatoid arthritis. Acta Odontol Scand. 1987;45(5):329-36. https://doi.org/10.3109/00016358709096355
• 25. Gatt A, Mercieca C, Borg A, Grech A, Camilleri L, Gatt C, Chockalin-gam N, Formosa C. A comparison of thermographic characteristics of the hands and wrists of rheumatoid arthritis patients and healthy controls. Sci Rep. 2019;25;9(1):17204. https://doi.org/10.1038/s41598-019-53598-0
• 26. Fischer M, Mielke H, Glaefke S, Deicher H. Generalized vasculopa-thy and finger blood flow abnormalities in rheumatoid arthritis. J Rheumatol. 1984;11(1):33-7.
• 27. Anjos A, Leite R, Cancela ML, Shahbazkia H. MAQ – A bioinformat-ics tool for automatic macroarray analysis. International Journal of Computer Applications 2010;4(3). https://doi.org/10.5120/843-1066
• 28. Rusch D, Follmann M, Boss B, Neeck G. Dynamic thermography of the knee joints in rheumatoid arthritis (RA) in the course of the first therapy of the patient with methylprednisolone. Z Rheumatol. 2000;59(2):II/131-5. https://doi.org/10.1007/s003930070009
• 29. Nowakowski A. Problems of active dynamic thermography meas-urement standarization in medicine. Pomiary Automatyka Robotyka 2021;3: 51-56. https://doi.org/10.14313/PAR_241/51