{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T18:05:06Z","timestamp":1784829906210,"version":"3.55.0"},"reference-count":28,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background: Rheumatoid arthritis (RA) is a slow progressive autoimmune disease. RA disproportionately affects women due to hormonal and immune variations. During pregnancy, hormonal and immune system changes vary drastically and may lead to RA. Traditional diagnostic techniques are blood biomarkers and clinical assessments. The above methods fail to detect RA at earlier stage due to subclinical inflammation and pregnancy-related physiological changes. Thermal imaging, as a non-invasive and radiation-free approach, can reveal temperature asymmetries across inflamed joints, offering a safer diagnostic pathway for pregnant women. Objective: In this paper, non-invasive RA detection is performed using finger, leg and hand thermal images. A pregnancy-aware rheumatoid arthritis (PARA) diagnostic framework is proposed. The PARA framework uses hybrid deep learning algorithms to classify RA inflammation states, such as normal, moderate and high. Methods: Using the PARA framework, thermal images were obtained from pregnant women. A total of 28 major bone joints were captured across four physiological states, including normal and before pregnancy. The thermal images were obtained from normal women, pregnant women, and women after pregnancy using a smartphone -based high-resolution USB thermal camera. Preprocessing was performed using bilateral, Non-Local Means (NLM), and guided filters to enhance thermal images for clarity. The guided filter preserves the edges and suppresses noise. Our proposed Denoising Convolutional Neural Network (DnCNN) algorithm was applied to preprocessed images to extract inflammation-sensitive thermal features. Finally, Multiple Linear Regression (MLR) was employed to predict the inflammation scale using the statistical values from the DnCNN-processed images. Results: The regression analysis revealed a strong correlation between thermal gradients and inflammatory severity across elbow, hand, and knee joints; i.e., the K-fold accuracy was 93.84 \u00b1 0.71. The Modified Clinical Discord Activity Index (MCDAI) categorizes inflammation as low, moderate, and high, and these values were used in the PARA framework for inflammation level prediction supporting early clinical decision-making. Conclusion: The proposed PARA framework has high diagnostic potential to classify RA stages in pregnant women through a non-invasive and pregnancy-specific assessment. The PARA framework reduces dependency on laboratory tests and supports timely therapeutic interventions.<\/jats:p>","DOI":"10.3390\/s26154670","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T17:13:17Z","timestamp":1784826797000},"page":"4670","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Framework for Rheumatoid Arthritis Assessment Using Thermal Images Based on DnCNN-MLR Hybrid Algorithm and Joint Temperature Indexing"],"prefix":"10.3390","volume":"26","author":[{"given":"Sujatha","family":"Binny","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P. Sardar","family":"Maran","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College (Autonomous), Avadi, Chennai 600062, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lin, W.-Y., Lee, Y.-C., Chowdhury, A.A., Burns, L.M., and Wang, H.-Y. (2026). County-Level Association Between Social Vulnerability and Rheumatoid Arthritis-Related Mortality in the United States. Med. Sci., 14.","DOI":"10.3390\/medsci14020314"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Manjappa, A.B., Nitilapura, N., Shetty, S., Rao, S., Babu, S., Shetty, J., Shetty, R., and Basavarajappa, M.K. (2026). Differential Biological and Molecular Profiling of Mesenchymal Progenitor Cells in Cartilage from Osteoarthritis and Rheumatoid Arthritis: An In Vitro Study. Int. J. Mol. Sci., 27.","DOI":"10.3390\/ijms27125252"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yao, M., Li, W., Xin, Y., Li, D., Yang, L., and Zhu, J. (2026). High-Frequency Ultrasound Radiomics Combined with Clinical Features for Detecting OMERACT-Defined Metacarpophalangeal Joint Cartilage Damage in Early Rheumatoid Arthritis. Diagnostics, 16.","DOI":"10.3390\/diagnostics16121758"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Guo, H., Wang, H., and Zheng, G. (2021, January 9\u201312). Ternate Chinese Medicines Alleviate Joint Pain via Inhibiting Rheumatoid Arthritis Pathway. Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Houston, TX, USA.","DOI":"10.1109\/BIBM52615.2021.9669333"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Perez-Siguas, R., Matta-Solis, E., Remuzgo-Artezano, A., Matta-Solis, H., Matta-Perez, H., and Perez-Siguas, L. (2023, January 5\u20136). Hand X-ray Image Processing System for the Early Detection of Rheumatoid Arthritis. Proceedings of the 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India.","DOI":"10.1109\/ICAECT57570.2023.10118003"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Omelchenko, V., Kurochkina, Y., Akimova, A., Letyagina, E., Shaidurova, M., and Korolev, M. (2022, January 11\u201313). Body Composition in Women with Rheumatoid Arthritis. Proceedings of the 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), Yekaterinburg, Russia.","DOI":"10.1109\/SIBIRCON56155.2022.10016974"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6017","DOI":"10.1109\/JBHI.2025.3554364","article-title":"ExSMART-PreRA: Explainable Survival and Risk Assessment Using Machine Learning","volume":"29","author":"Salehi","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hioki, Y., Makino, K., Koyama, K., Haro, H., and Terada, H. (2021, January 9\u201311). Evaluation Method of Rheumatoid Arthritis by X-ray Photograph Using Deep Learning. Proceedings of the 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech), Nara, Japan.","DOI":"10.1109\/LifeTech52111.2021.9391953"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Pathak, S. (2024, January 9). Deep Learning Approach to Early Detection of Rheumatoid Arthritis. Proceedings of the 2024 IEEE Integrated STEM Education Conference (ISEC), Princeton, NJ, USA.","DOI":"10.1109\/ISEC61299.2024.10665030"},{"key":"ref_10","unstructured":"Ou, Y., Ambalathankandy, P., Furuya, R., Kawada, S., Kamishima, T., and Ikebe, M. (2022, January 10\u201313). Joint Space Narrowing Progression Quantification. Proceedings of the 2022 14th Biomedical Engineering International Conference (BMEiCON), Songkhla, Thailand."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Grace, A.E., and Ramachandran, T.P. (2025, January 27\u201328). Pathway-Based Gene Prioritization and Machine Learning for RA Analysis. Proceedings of the 2025 International Conference on Frontier Technologies and Solutions (ICFTS), Chennai, India.","DOI":"10.1109\/ICFTS62006.2025.11032011"},{"key":"ref_12","unstructured":"Shah, P., Kumar, Y., and Bhowmik, T. (2025, January 16\u201318). Deep Learning for Arthritis Diagnosis. Proceedings of the 2025 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), Bhubaneswar, India."},{"key":"ref_13","unstructured":"Ali, N.M. (2023, January 15\u201316). Early Rheumatoid Arthritis Detection by miRNA Using CNN-LSTM. Proceedings of the 2023 Intelligent Methods, Systems, and Applications (IMSA), Giza, Egypt."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ojha, S., Anand, S., and Kanisha, B. (2023, January 4\u20136). Prediction of Rheumatoid Arthritis Using Deep Learning. Proceedings of the, 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India.","DOI":"10.1109\/ICAAIC56838.2023.10141208"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Padki, P., and Selvam, S. (2023, January 19\u201321). Prediction of Rheumatoid Arthritis Susceptibility Using Gene Mutation Rate. Proceedings of the 2023 International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), Bangalore, India.","DOI":"10.1109\/ICAECIS58353.2023.10170167"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Arcot, S.V., Buvaneswari, T., Kavitha, K.R., Manickavasagam, R., M, S., and Muthulekshmi, M. (2025, January 7\u20138). Enhanced Rheumatoid Arthritis Treatment Optimization Through Deep Learning. Proceedings of the 2025 3rd International Conference on Disruptive Technologies (ICDT), Greater Noida, India.","DOI":"10.1109\/ICDT63985.2025.10986740"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Naz, R., Ahmad, M., Sakarkar, G., Jikar, T., Parhatay, S., Mishra, K., and Deshmukh, S. (2023, January 28\u201329). Rheumatoid Arthritis Classification Using Thermal Imaging. Proceedings of the 2023 11th International Conference on Emerging Trends in Engineering & Technology\u2014Signal and Information Processing (ICETET-SIP), Nagpur, India.","DOI":"10.1109\/ICETET-SIP58143.2023.10151532"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/JBHI.2022.3217685","article-title":"Sub-Pixel Accurate Quantification of Joint Space Narrowing","volume":"27","author":"Ou","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mashkoor, M., Zhang, S., and Stensballe, A. (2026). Bridging the Precision Gap in Rheumatoid Arthritis: Spatial Transcriptomics, Spatial Proteomics, and Artificial Intelligence in Precision Health. Biomedicines, 14.","DOI":"10.3390\/biomedicines14030668"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Imrak, E.D., and Aktas, \u0130. (2026). Retrospective Analysis of Hematological Parameter Changes in DMARD-Naive Rheumatoid Arthritis Patients Treated with Methotrexate: Correlation with Disease Activity and Treatment Outcomes. Biomedicines, 14.","DOI":"10.3390\/biomedicines14030625"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, L., Shen, T., Li, Z., Guo, Q., and Pang, Q. (2026). Anti-Inflammatory Effects of Curcumin via the Nrf2-cGAS-STING-NF-\u03baB Pathway in MH7A Rheumatoid Arthritis Fibroblast-like Synoviocytes. Biomedicines, 14.","DOI":"10.3390\/biomedicines14030611"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rizaj, D., Kryeziu, A., Kelmendi, A., Raci, B., Kryeziu, S., and Baftijari, V. (2026). The Role of NLR, PLR, SII and CRP Pre- and Post-Treatment with Infliximab in Rheumatoid Arthritis. Biomedicines, 14.","DOI":"10.3390\/biomedicines14010255"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Karsulovic, C., Tempio, F., Lopez, M., Guerrero, J., Joo Hu, K.W.K., and Goecke, A. (2025). Enhanced mTORC1 Signaling in Inflammatory Monocytes Links Systemic Inflammation to Cardiovascular Disease in Rheumatoid Arthritis. Biomedicines, 13.","DOI":"10.3390\/biomedicines13112578"},{"key":"ref_24","first-page":"53","article-title":"Thermal symmetry of the upper and lower extremities in healthy subjects","volume":"22","author":"Vardasca","year":"2012","journal-title":"Thermol. Int."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1350045","DOI":"10.1142\/S0219519413500450","article-title":"New standards for fever screening with thermal imaging systems","volume":"13","author":"Ring","year":"2013","journal-title":"J. Mech. Med. Biol."},{"key":"ref_26","first-page":"154","article-title":"Symmetry of temperature distribution in the upper and lower extremities","volume":"18","author":"Vardasca","year":"2008","journal-title":"Thermol. Int."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3413","DOI":"10.1109\/TMI.2021.3085712","article-title":"MoNuSAC2020: A multi-organ nuclei segmentation and classification challenge","volume":"40","author":"Verma","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1109\/TIP.2007.891787","article-title":"Vertex-based diffusion for 3-D mesh denoising","volume":"16","author":"Zhang","year":"2007","journal-title":"IEEE Trans. Image Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/15\/4670\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T17:16:23Z","timestamp":1784826983000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/15\/4670"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,23]]},"references-count":28,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["s26154670"],"URL":"https:\/\/doi.org\/10.3390\/s26154670","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,23]]}}}