Comparing the Heating Rate of the Proximal Phalanx of the Fingers in Rheumatoid Arthritis and Healthy Subjects

1. McInnes IB, Schett G. The pathogenesis of rheumatoid arthritis. N Engl J Med. 2011;365:2205–19. https://doi.org/10.1056/NEJMra1004965 Search in Google Scholar

2. Branco JHL, Branco RLL, Siqueira TC, de Souza LC, Dalago KMS, Andrade A. Clinical applicability of infrared thermography in rheumatic diseases: A systematic review. J Therm Biol. 2022; 104:103172. https://doi.org/10.1016/j.jtherbio.2021.103172 Search in Google Scholar

3. Sanchez BM, Lesch M, Brammer D, Bove SE, Thiel M, Kilgore KS. Use of a portable thermal imaging unit as a rapid, quantitative method 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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

6. Pauk J, Ihnatouski M, Wasilewska A. Detection of inflammation from finger temperature profile in rheumatoid arthritis. Med Biol Eng Comput. 2019;57(12):2629-2639. https://doi.org/10.1007/s11517-019-02055-1. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

9. Bardhan S, Bhowmik MK. 2-Stage classification of knee joint thermograms for rheumatoid arthritis prediction in subclinical inflammation. Australas Phys Eng Sci Med. 2019;42(1):259-277. https://doi.org/10.1007/s13246-019-00726-9 Search in Google Scholar

10. Ahalya RK, Snekhalatha U, Dhanraj VJ. Automated segmentation and classification of hand thermal images in rheumatoid arthritis using machine learning algorithms: A comparison with quantum machine learning technique. Therm Biol. 2023;111:103404. https://doi.org/10.1016/j.jtherbio.2022.103404 Search in Google Scholar

11. Snekhalatha U, Anburajan M, Sowmiya V, Venkatraman B, Menaka M: Automated hand thermal image segmentation and feature extraction in the evaluation of rheumatoid arthritis, Proc Inst Mech Eng H 2015;229(4):319-31. https://doi.org/10.1177/0954411915580809 Search in Google Scholar

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 Search in Google Scholar

13. Venerito V, Angelini O, Cazzato G, Lopalco G, Maiorano E, Cimmino A, et al. A convolutional neural network with transfer learning for automatic 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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

16. Fukae J, Isobe M, Hattori T, Fujieda Y, Kono M, Abe N, et al. Convolutional neural network for classification of two-dimensional array images generated from clinical information may support diagnosis of rheumatoid arthritis. Sci Rep. 2020;10:5648. https://doi.org/10.1038/s41598-020-62634-3 Search in Google Scholar

17. Üreten K, Erbay H, Maraş HH. Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network. Clin Rheumatol. 2020;39:969–74. https://doi.org/10.1007/s10067-019-04487-4 Search in Google Scholar

18. Christensen ABH, Just SA, Andersen JKH, Savarimuthu TR. Applying 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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

21. Mountz JM, Alavi A, Mountz JD. Emerging optical and nuclear medicine imaging methods in rheumatoid arthritis. Nat Rev Rheumatol. 2012;8(12):719-28. https://doi.org/10.1038/nrrheum.2012.148 Search in Google Scholar

22. Tan YK, Hong C, Li H, Allen JC Jr, Thumboo J. A novel use of combined thermal and ultrasound imaging in detecting joint inflammation in rheumatoid arthritis. Eur J Radiol. 2021;134:109421. https://doi.org/10.1016/j.ejrad.2020.109421 Search in Google Scholar

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. Search in Google Scholar

24. Tegelberg A, Kopp S. Skin surface temperature over the temporomandibular and metacarpophalangeal joints in individuals with rheumatoid arthritis. Acta Odontol Scand. 1987;45(5):329-36. https://doi.org/10.3109/00016358709096355 Search in Google Scholar

25. Gatt A, Mercieca C, Borg A, Grech A, Camilleri L, Gatt C, Chockalingam 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 Search in Google Scholar

26. Fischer M, Mielke H, Glaefke S, Deicher H. Generalized vasculopathy and finger blood flow abnormalities in rheumatoid arthritis. J Rheumatol. 1984;11(1):33-7. Search in Google Scholar

27. Anjos A, Leite R, Cancela ML, Shahbazkia H. MAQ – A bioinformatics tool for automatic macroarray analysis. International Journal of Computer Applications 2010;4(3). https://doi.org/10.5120/843-1066 Search in Google Scholar

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 Search in Google Scholar

29. Nowakowski A. Problems of active dynamic thermography measurement standarization in medicine. Pomiary Automatyka Robotyka 2021;3: 51-56. https://doi.org/10.14313/PAR_241/51 Search in Google Scholar