{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T05:46:06Z","timestamp":1782366366373,"version":"3.54.5"},"reference-count":95,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:00Z","timestamp":1781740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Arthropathies are a major global health challenge because of their high prevalence, chronic progression, and significant impact on quality of life and health systems. Therefore, prompt and accurate diagnosis is critical for slowing disease progression and improving outcomes. Traditional imaging modalities, such as ultrasound and magnetic resonance imaging, suffer from significant limitations, including operator dependence, limited accessibility, high cost, and limited reproducibility. Infrared thermography has become a promising non-invasive imaging technique for identifying thermal variations linked to inflammatory and metabolic processes. Advances in quantitative thermography, automated segmentation, and artificial intelligence have greatly enhanced its clinical applicability. This review summarizes recent advances in thermography-based biomarkers, including region-of-interest-derived metrics, asymmetry indices, hotspot burden, spatial and texture descriptors, and composite thermographic scores. It discusses the role of machine learning and deep learning in prediction, phenotyping, and multimodal integration with clinical, laboratory, and imaging data. Heterogeneity of protocols, variability in measurements, domain shift, validation design, overfitting, and reporting quality are also addressed. Overall, thermography combined with AI is highly promising as an adjunct to early diagnosis, assessment of disease activity, and follow-up in arthropathies. However, clinical application at a large scale requires strict standardization, external validation, transparent reporting, and well-elucidated, reproducible analytical processes.<\/jats:p>","DOI":"10.3390\/jimaging12060270","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T09:18:43Z","timestamp":1781860723000},"page":"270","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5595-1135","authenticated-orcid":false,"given":"Constantin-Adrian","family":"Andrei","sequence":"first","affiliation":[{"name":"Faculty of Medicine, The \u201cCarol Davila\u201d University of Medicine and Pharmacy, 050474 Bucharest, Romania"},{"name":"\u201cFoisor\u201d Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, 021382 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7403-1684","authenticated-orcid":false,"given":"Serban","family":"Dragosloveanu","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, The \u201cCarol Davila\u201d University of Medicine and Pharmacy, 050474 Bucharest, Romania"},{"name":"\u201cFoisor\u201d Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, 021382 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3442-1139","authenticated-orcid":false,"given":"Alex-Gabriel","family":"Grigore","sequence":"additional","affiliation":[{"name":"\u201cFoisor\u201d Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, 021382 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2537-6713","authenticated-orcid":false,"given":"Andreea Alexandra","family":"Anghel","sequence":"additional","affiliation":[{"name":"Faculty of Automation, Computer Science, Electrical and Electronic Engineering, \u201cDun\u0103rea de Jos\u201d University of Galati, 800008 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atanasie-Andrei","family":"Gogu","sequence":"additional","affiliation":[{"name":"\u201cFoisor\u201d Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, 021382 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7400-2904","authenticated-orcid":false,"given":"Rares-Mircea","family":"Birlutiu","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, The \u201cCarol Davila\u201d University of Medicine and Pharmacy, 050474 Bucharest, Romania"},{"name":"\u201cFoisor\u201d Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, 021382 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christiana Diana Maria","family":"Dragosloveanu","sequence":"additional","affiliation":[{"name":"Faculty of Dentistry, The \u201cCarol Davila\u201d University of Medicine and Pharmacy, 041292 Bucharest, Romania"},{"name":"Department of Ophthalmology, Clinical Hospital for Ophthalmological Emergencies, 010464 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1849-3072","authenticated-orcid":false,"given":"Catalin","family":"Anghel","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, \u201cDun\u0103rea de Jos\u201d University of Galati, 800146 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5987-1789","authenticated-orcid":false,"given":"Adrian","family":"Iftime","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, The \u201cCarol Davila\u201d University of Medicine and Pharmacy, 050474 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Romica","family":"Cergan","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, The \u201cCarol Davila\u201d University of Medicine and Pharmacy, 050474 Bucharest, Romania"},{"name":"\u201cFoisor\u201d Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, 021382 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4530-7965","authenticated-orcid":false,"given":"Constantin","family":"Caruntu","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, The \u201cCarol Davila\u201d University of Medicine and Pharmacy, 050474 Bucharest, Romania"},{"name":"Department of Dermatology, \u201cProf. N.C. Paulescu\u201d National Institute of Diabetes, Nutrition and Metabolic Diseases, 011233 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7676-6393","authenticated-orcid":false,"given":"Cristian","family":"Scheau","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, The \u201cCarol Davila\u201d University of Medicine and Pharmacy, 050474 Bucharest, Romania"},{"name":"\u201cFoisor\u201d Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, 021382 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"231","DOI":"10.31138\/mjr.30.4.231","article-title":"Implementation of Physical Activity into routine Clinical pracTice in Rheumatic Musculoskeletal Disease: The IMPACT-RMD study protocol and rationale","volume":"30","author":"Metsios","year":"2019","journal-title":"Mediterr. J. Rheumatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/s40364-025-00760-8","article-title":"Global, regional and national burden of rheumatoid arthritis from 1990 to 2021, with projections of incidence to 2050: A systematic and comprehensive analysis of the Global Burden of Disease study 2021","volume":"13","author":"Ma","year":"2025","journal-title":"Biomark. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gullick, N., Ibrahim, F., Scott, I.C., Vincent, A., Cope, A.P., Garrood, T., Panayi, G.S., Scott, D.L., and Kirkham, B. (2019). Real world long-term impact of intensive treatment on disease activity, disability and health-related quality of life in rheumatoid arthritis. BMC Rheumatol., 3.","DOI":"10.1186\/s41927-019-0054-y"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1186\/ar3845","article-title":"Early diagnosis to enable early treatment of pre-osteoarthritis","volume":"14","author":"Chu","year":"2012","journal-title":"Arthritis Res. Ther."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1007\/s11307-020-01510-y","article-title":"Lactate Production Precedes Inflammatory Cell Recruitment in Arthritic Ankles: An Imaging Study","volume":"22","author":"Neveu","year":"2020","journal-title":"Mol. Imaging Biol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e003880","DOI":"10.1136\/rmdopen-2023-003880","article-title":"Metabolic and molecular imaging in inflammatory arthritis","volume":"10","author":"Sousa","year":"2024","journal-title":"RMD Open"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Surowiec, I., \u00c4rlestig, L., Rantap\u00e4\u00e4-Dahlqvist, S., and Trygg, J. (2016). Metabolite and Lipid Profiling of Biobank Plasma Samples Collected Prior to Onset of Rheumatoid Arthritis. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0164196"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9259","DOI":"10.1038\/srep09259","article-title":"Biomarkers of early stage osteoarthritis, rheumatoid arthritis and musculoskeletal health","volume":"5","author":"Ahmed","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"22115","DOI":"10.1038\/s41598-022-26728-4","article-title":"Receiver operating characteristic analysis using a novel combined thermal and ultrasound imaging for assessment of disease activity in rheumatoid arthritis","volume":"12","author":"Tan","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"R33","DOI":"10.1088\/0967-3334\/33\/3\/R33","article-title":"Infrared thermal imaging in medicine","volume":"33","author":"Ring","year":"2012","journal-title":"Physiol. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1007\/s00296-026-06093-5","article-title":"Infrared thermography in the detection of arthritis: A systematic review of diagnostic performance compared with joint ultrasound","volume":"46","author":"Bastelberger","year":"2026","journal-title":"Rheumatol. Int."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"\u00c1vila-Camacho, F.J., Moreno-Villalba, L.M., Cortes-Altamirano, J.L., Alfaro-Rodr\u00edguez, A., Lara-Figueroa, H.-N., Herrera-L\u00f3pez, M.-E., and Romero-Morelos, P. (2025). Infrared Thermography and Deep Learning Prototype for Early Arthritis and Arthrosis Diagnosis: Design, Clinical Validation, and Comparative Analysis. Technologies, 13.","DOI":"10.3390\/technologies13100447"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pauk, J., Wasilewska, A., and Ihnatouski, M. (2019). Infrared Thermography Sensor for Disease Activity Detection in Rheumatoid Arthritis Patients. Sensors, 19.","DOI":"10.3390\/s19163444"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1007\/s40744-022-00475-4","article-title":"Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review","volume":"9","author":"Momtazmanesh","year":"2022","journal-title":"Rheumatol. Ther."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Morales-Ivorra, I., Taverner, D., Codina, O., Castell, S., Fischer, P., Onken, D., Mart\u00ednez-Osuna, P., Battioui, C., and Mar\u00edn-L\u00f3pez, M.A. (2024). External Validation of the Machine Learning-Based Thermographic Indices for Rheumatoid Arthritis: A Prospective Longitudinal Study. Diagnostics, 14.","DOI":"10.3390\/diagnostics14131394"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e002458","DOI":"10.1136\/rmdopen-2022-002458","article-title":"Assessment of inflammation in patients with rheumatoid arthritis using thermography and machine learning: A fast and automated technique","volume":"8","author":"Moragues","year":"2022","journal-title":"RMD Open"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1790s","DOI":"10.1177\/19476035211063862","article-title":"Infrared Thermography for the Evaluation of Inflammatory and Degenerative Joint Diseases: A Systematic Review","volume":"13","author":"Schiavon","year":"2021","journal-title":"Cartilage"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lubkowska, A., and Pluta, W. (2022). Infrared Thermography as a Non-Invasive Tool in Musculoskeletal Disease Rehabilitation\u2014The Control Variables in Applicability\u2014A Systematic Review. Appl. Sci., 12.","DOI":"10.3390\/app12094302"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"159","DOI":"10.31138\/mjr.271024.ita","article-title":"Clinical Application of Infrared Thermography in Rheumatic Diseases: A Systematic Review","volume":"36","author":"Nallathambi","year":"2025","journal-title":"Mediterr. J. Rheumatol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105496","DOI":"10.1016\/j.jbspin.2022.105496","article-title":"An update on thermal imaging in rheumatoid arthritis","volume":"90","author":"Kow","year":"2023","journal-title":"Jt. Bone Spine"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Andrei, C.-A., Dragosloveanu, S., Grigore, A.-G., Timofticiuc, I.-A., Birlutiu, R.-M., Anghel, C., Moise, A.-E., Gherghe, M.E., Pulik, \u0141., and Iftime, A. (2026). Cutaneous Thermography in the Diagnosis and Management of Arthropathies: Pathophysiology, Diagnostic Pathways, and Multimodal Imaging Correlations. Appl. Sci., 16.","DOI":"10.3390\/app16115709"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s40001-025-02278-z","article-title":"Infrared thermography in clinical practice: A literature review","volume":"30","author":"Liu","year":"2025","journal-title":"Eur. J. Med. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103932","DOI":"10.1016\/j.jtherbio.2024.103932","article-title":"Thermal threshold for knee osteoarthritis people evaluated with infrared thermography: A scoping review","volume":"123","author":"Petrigna","year":"2024","journal-title":"J. Therm. Biol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Keszty\u00fcs, D., Brucher, S., Wilson, C., and Keszty\u00fcs, T. (2023). Use of Infrared Thermography in Medical Diagnosis, Screening, and Disease Monitoring: A Scoping Review. Medicina, 59.","DOI":"10.3390\/medicina59122139"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102449","DOI":"10.1016\/j.autrev.2019.102449","article-title":"Thermography in systemic sclerosis patients and other rheumatic diseases: Diagnosis, disease activity assessment, and therapeutic monitoring","volume":"19","author":"Sciascia","year":"2020","journal-title":"Autoimmun. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"20170218","DOI":"10.1515\/jbcpp-2017-0218","article-title":"Clinical assessment of arthritic knee pain by infrared thermography","volume":"30","author":"Fokam","year":"2018","journal-title":"J. Basic Clin. Physiol. Pharmacol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nowakowski, A.Z., and Kaczmarek, M. (2025). Artificial Intelligence in IR Thermal Imaging and Sensing for Medical Applications. Sensors, 25.","DOI":"10.3390\/s25030891"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1049\/smt2.12079","article-title":"Non-invasive assessment of knee osteoarthritis patients using thermal imaging","volume":"16","author":"Lohchab","year":"2021","journal-title":"IET Sci. Meas. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e200029","DOI":"10.1148\/ryai.2020200029","article-title":"Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers","volume":"2","author":"Mongan","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1007122","DOI":"10.37349\/emd.2026.1007122","article-title":"Standardizing infrared thermography for occupational applications: An integrative narrative review of protocol quality, thermal metrics, and multimodal integration in work-related musculoskeletal disorders","volume":"4","author":"Ribeiro","year":"2026","journal-title":"Explor. Musculoskelet. Dis."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1080\/03009742.2025.2548061","article-title":"Understanding the use of thermography and its ability to predict ultrasound-detected joint inflammation at the metacarpophalangeal joint in patients with rheumatoid arthritis","volume":"55","author":"Tan","year":"2025","journal-title":"Scand. J. Rheumatol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1007\/s10278-025-01548-w","article-title":"Best Practices and Checklist for Reviewing Artificial Intelligence-Based Medical Imaging Papers: Classification","volume":"39","author":"Kline","year":"2026","journal-title":"J. Imaging Inform. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1088\/0143-0815\/11\/4A\/310","article-title":"Quantitative thermal imaging","volume":"11","author":"Ring","year":"1990","journal-title":"Clin. Phys. Physiol. Meas."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"141","DOI":"10.3349\/ymj.2022.63.2.141","article-title":"The Value of Thermal Imaging for Knee Arthritis: A Single-Center Observational Study","volume":"63","author":"Ahn","year":"2022","journal-title":"Yonsei Med. J."},{"key":"ref_35","first-page":"125","article-title":"The Glamorgan Protocol for recording and evaluation of thermal images of the human body","volume":"18","author":"Ammer","year":"2008","journal-title":"Thermol. Int."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.infrared.2012.03.007","article-title":"Medical applications of infrared thermography: A review","volume":"55","author":"Lahiri","year":"2012","journal-title":"Infrared Phys. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, H.Y., Son, S., Yoo, B.R., and Youk, T.-M. (2023). Reference Standard for Digital Infrared Thermography of the Surface Temperature of the Lower Limbs. Bioengineering, 10.","DOI":"10.3390\/bioengineering10030283"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"S189","DOI":"10.1016\/j.mjafi.2022.05.011","article-title":"Thermal imaging in rheumatoid arthritis knee joints and its correlation with power Doppler ultrasound","volume":"79","author":"Vasdev","year":"2023","journal-title":"Med. J. Armed Forces India"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"81","DOI":"10.3899\/jrheum.210077","article-title":"A Novel Algorithm Using Within-leg Calibration for Enhanced Accuracy of Detection of Arthritis by Infrared Thermal Imaging in Children","volume":"49","author":"Zhao","year":"2022","journal-title":"J. Rheumatol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tan, Y.K., and Thumboo, J. (2024). Understanding Ultrasound Power Doppler Synovitis at Clinically Quiescent Joints and Thermographic Joint Inflammation Assessment in Patients with Rheumatoid Arthritis. Diagnostics, 14.","DOI":"10.3390\/diagnostics14212384"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"De Marziani, L., Boffa, A., Angelelli, L., Andriolo, L., Di Martino, A., Zaffagnini, S., and Filardo, G. (2023). Infrared Thermography in Symptomatic Knee Osteoarthritis: Joint Temperature Differs Based on Patient and Pain Characteristics. J. Clin. Med., 12.","DOI":"10.3390\/jcm12062319"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"104029","DOI":"10.1016\/j.medengphy.2023.104029","article-title":"An application based on the analysis of thermograms to calculate the thermographic index for pain and swelling assessment","volume":"119","year":"2023","journal-title":"Med. Eng. Phys."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Arnal, M., Bourrilhon, C., Beauchamps, V., Sauvet, F., Zahouani, H., and Thieulin, C. (2024). An Automated Semantic Segmentation Methodology for Infrared Thermography Analysis of the Human Hand. J. Sens. Actuator Netw., 13.","DOI":"10.3390\/jsan13060086"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1177\/0954411915580809","article-title":"Automated hand thermal image segmentation and feature extraction in the evaluation of rheumatoid arthritis","volume":"229","author":"Snekhalatha","year":"2015","journal-title":"Proc. Inst. Mech. Eng. Part H J. Eng. Med."},{"key":"ref_45","first-page":"1802","article-title":"Performance of a novel high-resolution infrared thermography marker in detecting and assessing joint inflammation: A comparison with joint ultrasound","volume":"42","author":"Triantafyllias","year":"2024","journal-title":"Clin. Exp. Rheumatol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s11517-020-02294-7","article-title":"Computer-aided diagnosis based on hand thermal, RGB images, and grip force using artificial intelligence as screening tool for rheumatoid arthritis in women","volume":"59","author":"Alonso","year":"2021","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Son, S., Yoo, B.R., and Zhang, H.Y. (2023). Reference Standards for Digital Infrared Thermography Measuring Surface Temperature of the Upper Limbs. Bioengineering, 10.","DOI":"10.3390\/bioengineering10060671"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"17204","DOI":"10.1038\/s41598-019-53598-0","article-title":"A comparison of thermographic characteristics of the hands and wrists of rheumatoid arthritis patients and healthy controls","volume":"9","author":"Gatt","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"212","DOI":"10.4103\/JMU.JMU_113_20","article-title":"Infrared Thermography and Ultrasonography of the Hands in Rheumatoid Arthritis Patients","volume":"29","author":"Yabunaka","year":"2021","journal-title":"J. Med. Ultrasound"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"e19843","DOI":"10.7717\/peerj.19843","article-title":"Thermography analysis as a tool for assessing thermal asymmetries and temperature changes after therapy in patients with stroke: A pilot study","volume":"13","year":"2025","journal-title":"PeerJ"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"488","DOI":"10.2967\/jnumed.118.222893","article-title":"Introduction to Radiomics","volume":"61","author":"Mayerhoefer","year":"2020","journal-title":"J. Nucl. Med."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, W., Guo, Y., and Jin, Q. (2023). Radiomics and Its Feature Selection: A Review. Symmetry, 15.","DOI":"10.3390\/sym15101834"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Tabassum, M., Suman, A.A., Suero-Molina, E., Pan, E., Di Ieva, A., and Liu, S. (2023). Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review. Cancers, 15.","DOI":"10.3390\/cancers15153845"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e002615","DOI":"10.1136\/rmdopen-2022-002615","article-title":"A Thermographic Disease Activity Index for remote assessment of rheumatoid arthritis","volume":"8","author":"Moragues","year":"2022","journal-title":"RMD Open"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1007\/s40744-024-00648-3","article-title":"Thermography at the Elbow Among Patients with Rheumatoid Arthritis: A Comparison with Ultrasound-Detected Joint Inflammation Findings","volume":"11","author":"Tan","year":"2024","journal-title":"Rheumatol. Ther."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5092064","DOI":"10.1155\/2018\/5092064","article-title":"Automated Region Extraction from Thermal Images for Peripheral Vascular Disease Monitoring","volume":"2018","author":"Gauci","year":"2018","journal-title":"J. Healthc. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Crisafulli, D., Spataro, M., De Marchis, C., Risitano, G., and Milone, D. (2024). A New Sensorized Approach Based on a DeepLabCut Model and IR Thermography for Characterizing the Thermal Profile in Knees During Exercise. Sensors, 24.","DOI":"10.3390\/s24237862"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Kesavapillai, A.R., Aslam, S.M., Umapathy, S., and Almutairi, F. (2024). RA-XTNet: A Novel CNN Model to Predict Rheumatoid Arthritis from Hand Radiographs and Thermal Images: A Comparison with CNN Transformer and Quantum Computing. Diagnostics, 14.","DOI":"10.3390\/diagnostics14171911"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"11349","DOI":"10.1007\/s10973-024-13343-w","article-title":"StereoThermoLegs: Label propagation with multimodal stereo cameras for automated annotation of posterior legs during running at different velocities","volume":"149","author":"Hillen","year":"2024","journal-title":"J. Therm. Anal. Calorim."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"103782","DOI":"10.1016\/j.tsep.2025.103782","article-title":"Advancing knee injury prevention and anomaly detection in rugby players through automated processing of infrared thermography: A novel biothermodynamics approach","volume":"65","author":"Usamentiaga","year":"2025","journal-title":"Therm. Sci. Eng. Prog."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Spataro, M., Crisafulli, D., De Marchis, C., Risitano, G., and Milone, D. (2025). Effect of Weight Distribution on Knee Joint Temperature Pattern Under Fatigue Condition. Eng. Proc., 85.","DOI":"10.3390\/engproc2025085043"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"10076","DOI":"10.1109\/TPAMI.2024.3435571","article-title":"Medical Image Segmentation Review: The Success of U-Net","volume":"46","author":"Azad","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"103404","DOI":"10.1016\/j.jtherbio.2022.103404","article-title":"Automated segmentation and classification of hand thermal images in rheumatoid arthritis using machine learning algorithms: A comparison with quantum machine learning technique","volume":"111","author":"Ahalya","year":"2023","journal-title":"J. Therm. Biol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"31960","DOI":"10.1038\/s41598-025-17901-6","article-title":"Comparing thermographic heat signatures with joint inflammation detected by ultrasound at the knees in patients with rheumatoid arthritis","volume":"15","author":"Tan","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"199","DOI":"10.4097\/kja.25075","article-title":"Comprehensive reporting guidelines and checklist for studies developing and utilizing artificial intelligence models","volume":"78","author":"Kwak","year":"2025","journal-title":"Korean J. Anesthesiol."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Gomes, V.M.d.S.A., Ten\u00f3rio, N., Cardozo da Silva, A.R., Patriota Oliveira, L.R., Souza da Silva, A.C., Netto Maia, J., Brioschi, M.L., and Dantas, D. (2024). Reproducibility of Thermography for Measuring Skin Temperature of Upper Limbs in Breast Cancer Survivors. Biomedicines, 12.","DOI":"10.3390\/biomedicines12112465"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"103345","DOI":"10.1016\/j.jtherbio.2022.103345","article-title":"Reproducibility of Skin Temperature Analyses by Novice and Experienced Evaluators Using Infrared Thermography","volume":"110","author":"Kunzler","year":"2022","journal-title":"J. Therm. Biol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"e15011","DOI":"10.7717\/peerj.15011","article-title":"Intra- and Inter-Session Reliability and Repeatability of an Infrared Thermography Device Designed for Materials to Measure Skin Temperature of the Triceps Surae Muscle Tissue of Athletes","volume":"11","year":"2023","journal-title":"PeerJ"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Tan, Y.K., and Thumboo, J. (2025). The EULAR-OMERACT joint-level scoring of ultrasound synovitis demonstrates good construct validity when tested at the patient-level in comparison with measures of disease activity and joint damage in patients with rheumatoid arthritis. Front. Med., 12.","DOI":"10.3389\/fmed.2025.1564381"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"e005002","DOI":"10.1136\/rmdopen-2024-005002","article-title":"Synovitis scoring in hand osteoarthritis with ultrasonography: The performance of the Global OMERACT\/EULAR Ultrasound Synovitis Score (GLOESS) is comparable to synovial thickening alone","volume":"10","author":"Kortekaas","year":"2024","journal-title":"RMD Open"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1001\/jama.2014.3266","article-title":"Fibromyalgia: A clinical review","volume":"311","author":"Clauw","year":"2014","journal-title":"JAMA"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"e547","DOI":"10.1097\/RHU.0000000000001575","article-title":"Clinical Manifestations and Diagnosis of Axial Spondyloarthritis","volume":"27","author":"Walsh","year":"2021","journal-title":"J. Clin. Rheumatol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"e082505","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: An updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","year":"2024","journal-title":"BMJ"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"15638","DOI":"10.1038\/s41598-023-42111-3","article-title":"RANet: A custom CNN model and quanvolutional neural network for the automated detection of rheumatoid arthritis in hand thermal images","volume":"13","author":"Ahalya","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_77","first-page":"303","article-title":"The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: A narrative review","volume":"31","author":"Simon","year":"2025","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Daskareh, M., Vakilpour, A., Barzegar-Golmoghani, E., Esmaeilian, S., Gilanchi, S., Ezzati, F., Alikhani, M., Rahmanipour, E., Amini, N., and Ghorbani, M. (2024). Predicting Rheumatoid Arthritis Development Using Hand Ultrasound and Machine Learning\u2014A Two-Year Follow-Up Cohort Study. Diagnostics, 14.","DOI":"10.3390\/diagnostics14111181"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1007\/s10067-025-07304-3","article-title":"Machine learning-based prediction model integrating ultrasound scores and clinical features for the progression to rheumatoid arthritis in patients with undifferentiated arthritis","volume":"44","author":"Hu","year":"2025","journal-title":"Clin. Rheumatol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1007\/s10586-025-05281-5","article-title":"Explainable artificial intelligence for medical imaging systems using deep learning: A comprehensive review","volume":"28","author":"Houssein","year":"2025","journal-title":"Clust. Comput."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"e222217","DOI":"10.1148\/radiol.222217","article-title":"Quantifying Uncertainty in Deep Learning of Radiologic Images","volume":"308","author":"Faghani","year":"2023","journal-title":"Radiology"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"3283","DOI":"10.1038\/s41591-025-03953-8","article-title":"The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence","volume":"31","author":"Sounderajah","year":"2025","journal-title":"Nat. Med."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"e081554","DOI":"10.1136\/bmj-2024-081554","article-title":"FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare","volume":"388","author":"Lekadir","year":"2025","journal-title":"BMJ"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"e078276","DOI":"10.1136\/bmj-2023-078276","article-title":"Developing clinical prediction models: A step-by-step guide","volume":"286","author":"Efthimiou","year":"2024","journal-title":"BMJ"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"100804","DOI":"10.1016\/j.patter.2023.100804","article-title":"Leakage and the reproducibility crisis in machine-learning-based science","volume":"4","author":"Kapoor","year":"2023","journal-title":"Patterns"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"m689","DOI":"10.1136\/bmj.m689","article-title":"Artificial intelligence versus clinicians: Systematic review of design, reporting standards, and claims of deep learning studies","volume":"268","author":"Nagendran","year":"2020","journal-title":"BMJ"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"e271","DOI":"10.1016\/S2589-7500(19)30123-2","article-title":"A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis","volume":"1","author":"Liu","year":"2019","journal-title":"Lancet Digit. Health"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"e074821","DOI":"10.1136\/bmj-2023-074821","article-title":"Evaluation of clinical prediction models (part 3): Calculating the sample size required for an external validation study","volume":"384","author":"Riley","year":"2024","journal-title":"BMJ"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"e210064","DOI":"10.1148\/ryai.210064","article-title":"External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review","volume":"4","author":"Yu","year":"2022","journal-title":"Radiol. Artif. Intell."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"106956","DOI":"10.1016\/j.csite.2025.106956","article-title":"Influence of infrared camera resolution and operator reproducibility in the assessment of skin temperature of the hands","volume":"74","author":"Riquet","year":"2025","journal-title":"Case Stud. Therm. Eng."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1016\/j.jacr.2024.04.027","article-title":"Establishing a Validation Infrastructure for Imaging-Based Artificial Intelligence Algorithms Before Clinical Implementation","volume":"21","author":"Ramwala","year":"2024","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"109256","DOI":"10.1016\/j.compbiomed.2024.109256","article-title":"A systematic review of generalization research in medical image classification","volume":"183","author":"Matta","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_93","unstructured":"Szumel, G., Guo, B., Lu, D., Gui, R., Wang, T., Konz, N., and Mazurowski, M. (2024). The Impact of Scanner Domain Shift on Deep Learning Performance in Medical Imaging: An Experimental Study. arXiv."},{"key":"ref_94","first-page":"440","article-title":"Adherence to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM): An umbrella review with a comprehensive two-level analysis","volume":"31","year":"2025","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1007\/s10278-024-01065-2","article-title":"Checklist for Reproducibility of Deep Learning in Medical Imaging","volume":"37","author":"Moassefi","year":"2024","journal-title":"J. Imaging Inform. Med."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/6\/270\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T04:49:49Z","timestamp":1782362989000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/6\/270"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,18]]},"references-count":95,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["jimaging12060270"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12060270","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,18]]}}}