{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,9]],"date-time":"2026-11-09T17:08:33Z","timestamp":1794244113988,"version":"4.9.0"},"reference-count":75,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T00:00:00Z","timestamp":1756252800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","diabetesresearchclinicalpractice.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Research and Clinical Practice"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1016\/j.diabres.2025.112446","type":"journal-article","created":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T14:54:00Z","timestamp":1756565640000},"page":"112446","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":17,"special_numbering":"C","title":["Analysis of foot thermography images of diabetic patients using artificial intelligence: a scoping review"],"prefix":"10.1016","volume":"228","author":[{"given":"Riki","family":"Wartakusumah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amika","family":"Yamada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroshi","family":"Noguchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4433-1608","authenticated-orcid":false,"given":"Makoto","family":"Oe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.diabres.2025.112446_b0005","doi-asserted-by":"crossref","first-page":"75","DOI":"10.4103\/0973-3930.62596","article-title":"Understanding diabetic foot","volume":"30","author":"Pendsey","year":"2010","journal-title":"Int J Diabetes Dev Ctries"},{"key":"10.1016\/j.diabres.2025.112446_b0010","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1056\/NEJMra1615439","article-title":"Diabetic foot ulcers and their recurrence","volume":"376","author":"Armstrong","year":"2017","journal-title":"N Engl J Med"},{"key":"10.1016\/j.diabres.2025.112446_b0015","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1530\/VB-20-0003","article-title":"Infrared thermography for monitoring severity and treatment of diabetic foot infections","volume":"2","author":"Hutting","year":"2020","journal-title":"Vasc Biol"},{"key":"10.1016\/j.diabres.2025.112446_b0020","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1177\/1932296819871270","article-title":"Infrared thermography and vascular disorders in diabetic feet","volume":"14","author":"Ilo","year":"2020","journal-title":"J Diabetes Sci Technol"},{"key":"10.1016\/j.diabres.2025.112446_b0025","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/9808295","article-title":"Establishing differences in thermographic patterns between the various complications in diabetic foot disease","volume":"2018","author":"Gatt","year":"2018","journal-title":"Int J Endocrinol"},{"key":"10.1016\/j.diabres.2025.112446_b0030","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1177\/193229681300700502","article-title":"Morphological pattern classification system for plantar thermography of patients with diabetes","volume":"7","author":"Mori","year":"2013","journal-title":"J Diabetes Sci Technol"},{"key":"10.1016\/j.diabres.2025.112446_b0035","doi-asserted-by":"crossref","first-page":"169","DOI":"10.5582\/ddt.2022.01050","article-title":"Development of a self-monitoring tool for diabetic foot prevention using smartphone-based thermography: Plantar thermal pattern changes and usability in the home environment","volume":"16","author":"Qin","year":"2022","journal-title":"Drug Discov Ther"},{"key":"10.1016\/j.diabres.2025.112446_b0040","doi-asserted-by":"crossref","first-page":"116","DOI":"10.12968\/jowc.2021.30.2.116","article-title":"Prevention of diabetic foot ulcers using a smartphone and mobile thermography: a case study","volume":"30","author":"Oe","year":"2021","journal-title":"J Wound Care"},{"key":"10.1016\/j.diabres.2025.112446_b0045","article-title":"DFU_MultiNet: a deep neural network approach for detecting diabetic foot ulcers through multi-scale feature fusion using the DFU dataset","volume":"8","author":"Biswas","year":"2023","journal-title":"Intell-Based Med"},{"key":"10.1016\/j.diabres.2025.112446_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.112296","article-title":"A novel lightweight deep learning framework with knowledge distillation for efficient diabetic foot ulcer detection","volume":"167","author":"Amjad","year":"2024","journal-title":"Appl Soft Comput"},{"key":"10.1016\/j.diabres.2025.112446_b0055","doi-asserted-by":"crossref","first-page":"1762","DOI":"10.3390\/s20061762","article-title":"Deep learning classification for diabetic foot thermograms","volume":"20","author":"Cruz-Vega","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.diabres.2025.112446_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104143","article-title":"Early detection of diabetic foot ulcers from thermal images using the bag of features technique","volume":"79","author":"Alshayeji","year":"2023","journal-title":"Biomed Signal Process Control"},{"key":"10.1016\/j.diabres.2025.112446_b0065","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2022.924546","article-title":"A review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring","volume":"13","author":"Kaselimi","year":"2022","journal-title":"Front Physiol"},{"key":"10.1016\/j.diabres.2025.112446_b0070","doi-asserted-by":"crossref","first-page":"2823","DOI":"10.3390\/app13052823","article-title":"Artificial intelligence based prediction of diabetic foot risk in patients with diabetes: a literature review","volume":"13","author":"Gosak","year":"2023","journal-title":"Appl Sci"},{"key":"10.1016\/j.diabres.2025.112446_b0075","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1186\/s12909-023-04698-z","article-title":"Revolutionizing healthcare: the role of artificial intelligence in clinical practice","volume":"23","author":"Alowais","year":"2023","journal-title":"BMC Med Educ"},{"key":"10.1016\/j.diabres.2025.112446_b0080","doi-asserted-by":"crossref","first-page":"757","DOI":"10.3390\/s23020757","article-title":"Feature ranking by variational dropout for classification using thermograms from diabetic foot ulcers","volume":"23","author":"Hernandez-Guedes","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.diabres.2025.112446_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104838","article-title":"A machine learning model for early detection of diabetic foot using thermogram images","volume":"137","author":"Khandakar","year":"2021","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.diabres.2025.112446_b0090","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s13755-022-00194-8","article-title":"Holistic multi-class classification & grading of diabetic foot ulcerations from plantar thermal images using deep learning","volume":"10","author":"Muralidhara","year":"2022","journal-title":"Health Inf Sci Syst"},{"key":"10.1016\/j.diabres.2025.112446_b0095","doi-asserted-by":"crossref","first-page":"3209","DOI":"10.3390\/biomedicines11123209","article-title":"State-of-the-art features for early-stage detection of diabetic foot ulcers based on thermograms","volume":"11","author":"Arteaga-Marrero","year":"2023","journal-title":"Biomedicines"},{"key":"10.1016\/j.diabres.2025.112446_b0100","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.3390\/s22051793","article-title":"Thermal change index-based diabetic foot thermogram image classification using machine learning techniques","volume":"22","author":"Khandakar","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.diabres.2025.112446_b0105","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.1007\/s40200-024-01452-0","article-title":"Digital infrared thermography and machine learning for diabetic foot assessment: thermal patterns and classification","volume":"23","author":"Castillo-Morquecho","year":"2024","journal-title":"J Diabetes Metab Disord"},{"key":"10.1016\/j.diabres.2025.112446_b0110","doi-asserted-by":"crossref","first-page":"7524","DOI":"10.3390\/app12157524","article-title":"A deep learning method for early detection of diabetic foot using decision fusion and thermal images","volume":"12","author":"Munadi","year":"2022","journal-title":"Appl Sci"},{"key":"10.1016\/j.diabres.2025.112446_b0115","doi-asserted-by":"crossref","first-page":"236","DOI":"10.3390\/a15070236","article-title":"Automatic classification of foot thermograms using machine learning techniques","volume":"15","author":"Filipe","year":"2022","journal-title":"Algorithms"},{"key":"10.1016\/j.diabres.2025.112446_b0120","doi-asserted-by":"crossref","first-page":"2637","DOI":"10.3390\/diagnostics13162637","article-title":"Automatic diabetic foot ulcer recognition using multi-level thermographic image data","volume":"13","author":"Khosa","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.diabres.2025.112446_b0125","doi-asserted-by":"crossref","unstructured":"Khullar V, Tiwari RG, Agarwal AK, Angurala M. Transferring Pre-trained Deep CNNs on Plantar Thermograms for Diabetic Foot Disease. In: Marriwala N, Tripathi CC, Jain S, Kumar D, editors. Mob. Radio Commun. 5G Netw., vol. 588, Singapore: Springer Nature Singapore; 2023, p. 97\u2013107. https:\/\/doi.org\/10.1007\/978-981-19-7982-8_9.","DOI":"10.1007\/978-981-19-7982-8_9"},{"key":"10.1016\/j.diabres.2025.112446_b0130","doi-asserted-by":"crossref","unstructured":"Prabhu MS, Verma S. A Deep Learning framework and its Implementation for Diabetic Foot Ulcer Classification. 2021 9th Int. Conf. Reliab. Infocom Technol. Optim. Trends Future Dir. ICRITO, Noida, India: IEEE; 2021, p. 1\u20135. https:\/\/doi.org\/10.1109\/ICRITO51393.2021.9596380.","DOI":"10.1109\/ICRITO51393.2021.9596380"},{"key":"10.1016\/j.diabres.2025.112446_b0135","doi-asserted-by":"crossref","unstructured":"Arnia F, Saddami K, Muharar R, Dwi Pratiwi DA, Nurdin Y. Diabetic Foot Ulcer Detection on Mobile Platforms through Thermal Imaging and Deep Learning. 2023 Int. Conf. Smart-Green Technol. Electr. Inf. Syst. ICSGTEIS, Badung, Bali, Indonesia: IEEE; 2023, p. 104\u20138. https:\/\/doi.org\/10.1109\/ICSGTEIS60500.2023.10424364.","DOI":"10.1109\/ICSGTEIS60500.2023.10424364"},{"key":"10.1016\/j.diabres.2025.112446_b0140","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.bbe.2022.03.001","article-title":"Fourier transform-based data augmentation in deep learning for diabetic foot thermograph classification","volume":"42","author":"Anaya-Isaza","year":"2022","journal-title":"Biocybern Biomed Eng"},{"key":"10.1016\/j.diabres.2025.112446_b0145","doi-asserted-by":"crossref","first-page":"368","DOI":"10.5114\/pjr\/189412","article-title":"Leveraging smart image processing techniques for early detection of foot ulcers using a deep learning network","volume":"89","author":"Verma","year":"2024","journal-title":"Pol J Radiol"},{"key":"10.1016\/j.diabres.2025.112446_b0150","article-title":"Deep neural net for identification of neuropathic foot in subjects with type 2 diabetes mellitus using plantar foot thermographic images","volume":"96","author":"Evangeline","year":"2024","journal-title":"Biomed Signal Process Control"},{"key":"10.1016\/j.diabres.2025.112446_b0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105341","article-title":"DFU_VIRNet: a novel Visible-InfraRed CNN to improve diabetic foot ulcer classification and early detection of ulcer risk zones","volume":"86","author":"Reyes-Lu\u00e9vano","year":"2023","journal-title":"Biomed Signal Process Control"},{"key":"10.1016\/j.diabres.2025.112446_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.smhl.2024.100502","article-title":"Towards accurate Diabetic Foot Ulcer image classification: leveraging CNN pre-trained features and extreme learning machine","volume":"33","author":"Arnia","year":"2024","journal-title":"Smart Health"},{"key":"10.1016\/j.diabres.2025.112446_b0165","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.infrared.2018.01.022","article-title":"Automated characterization of diabetic foot using nonlinear features extracted from thermograms","volume":"89","author":"Adam","year":"2018","journal-title":"Infrared Phys Technol"},{"key":"10.1016\/j.diabres.2025.112446_b0170","doi-asserted-by":"crossref","DOI":"10.1088\/2057-1976\/ad2479","article-title":"Automated prediction of diabetes mellitus using infrared thermal foot images: recurrent neural network approach","volume":"10","author":"Gulshan","year":"2024","journal-title":"Biomed Phys Eng Express"},{"key":"10.1016\/j.diabres.2025.112446_b0175","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.infrared.2015.09.022","article-title":"Automatic classification of thermal patterns in diabetic foot based on morphological pattern spectrum","volume":"73","author":"Hernandez-Contreras","year":"2015","journal-title":"Infrared Phys Technol"},{"key":"10.1016\/j.diabres.2025.112446_b0180","doi-asserted-by":"crossref","first-page":"747","DOI":"10.12785\/ijcds\/090421","article-title":"Automated classification of the diabetic foot using comprehensive encoding and feature transform techniques","volume":"9","author":"Prabhu","year":"2020","journal-title":"Int J Comput Digit Syst"},{"key":"10.1016\/j.diabres.2025.112446_b0185","doi-asserted-by":"crossref","unstructured":"Mounika N, Thirunavukkarasu U. Classification of healthy and diabetic mellitus individuals by extracted textural features from left plantar thermograms and classifying using svm and nb classifiers, Rajkot, India: 2023, p. 020097. https:\/\/doi.org\/10.1063\/5.0173193.","DOI":"10.1063\/5.0173193"},{"key":"10.1016\/j.diabres.2025.112446_b0190","doi-asserted-by":"crossref","first-page":"4077","DOI":"10.1520\/JTE20180503","article-title":"An efficient multiangle weight updated haralick and relevance vector machine algorithm for classifying diabetic foot from medical thermal image","volume":"47","author":"Gopinath","year":"2019","journal-title":"J Test Eval"},{"key":"10.1016\/j.diabres.2025.112446_b0195","first-page":"1","article-title":"Effectiveness of ConvNeXt variants in diabetic feet diagnosis using plantar thermal images","author":"Fasihi-Shirehjini","year":"2024","journal-title":"Quant InfraRed Thermogr J"},{"key":"10.1016\/j.diabres.2025.112446_b0200","doi-asserted-by":"crossref","unstructured":"Ansah P, Pattnayak P, Mohanty A, Das T, Patnaik S, Adjei PO, et al. Precision Medicine in Diabetes: A Machine Learning Model for Diabetic Foot Ulcer Prediction Using Keras TensorFlow. 2024 1st Int. Conf. Cogn. Green Ubiquitous Comput. IC-CGU, Bhubaneswar, India: IEEE; 2024, p. 1\u20136. https:\/\/doi.org\/10.1109\/IC-CGU58078.2024.10530735.","DOI":"10.1109\/IC-CGU58078.2024.10530735"},{"key":"10.1016\/j.diabres.2025.112446_b0205","doi-asserted-by":"crossref","unstructured":"Li Y, Pan, Li. Binary Classification of Diabetic Foot Thermograms Based on HDEfficientNetV2 Network. 2024 6th Int. Conf. Electron. Eng. Inform. (EEI), 2024, pp. 1411\u20131414. https:\/\/doi.org\/10.1109\/EEI63073.2024.10695995.","DOI":"10.1109\/EEI63073.2024.10695995"},{"key":"10.1016\/j.diabres.2025.112446_b0210","doi-asserted-by":"crossref","unstructured":"Kumar A, Nelson L, Singh S. ResNet-50 Transfer Learning Model for Diabetic Foot Ulcer Detection Using Thermal Images. 2023 2nd Int. Conf. Futur. Technol. INCOFT, Belagavi, Karnataka, India: IEEE; 2023, p. 1\u20135. https:\/\/doi.org\/10.1109\/INCOFT60753.2023.10425447.","DOI":"10.1109\/INCOFT60753.2023.10425447"},{"key":"10.1016\/j.diabres.2025.112446_b0215","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1080\/03091902.2022.2077997","article-title":"Diabetic foot thermal image segmentation using double Encoder-ResUnet (DE-ResUnet)","volume":"46","author":"Bouallal","year":"2022","journal-title":"J Med Eng Technol"},{"key":"10.1016\/j.diabres.2025.112446_b0220","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez SHR, Zequera D\u00edaz ML, Calder\u00f3n Bocanegra FC. Implementation of a Pattern Classifier on Thermograms from Plantar Region. In: Jarm T, \u0160merc R, Mahni\u010d-Kalamiza S, editors. 9th Eur. Med. Biol. Eng. Conf., vol. 113, Cham: Springer Nature Switzerland; 2024, p. 81\u201390. https:\/\/doi.org\/10.1007\/978-3-031-61628-0_9.","DOI":"10.1007\/978-3-031-61628-0_9"},{"key":"10.1016\/j.diabres.2025.112446_b0225","doi-asserted-by":"crossref","unstructured":"Mancilla RB, Daul C, Martinez JG, Salas LL, Wolf D, Hernandez AV. A Quantitative Method for the Detection of Temperature Differences on the Sole of the Foot in Diabetic Patients. 2021 Glob. Med. Eng. Phys. Exch. Am. Health Care Exch. GMEPEPAHCE, Sevilla, Spain: IEEE; 2021, p. 1\u20135. https:\/\/doi.org\/10.1109\/GMEPE\/PAHCE50215.2021.9434852.","DOI":"10.1109\/GMEPE\/PAHCE50215.2021.9434852"},{"key":"10.1016\/j.diabres.2025.112446_b0230","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1007\/s42600-022-00217-z","article-title":"Design and comparison of machine learning-based computer-aided diagnostic techniques to aid diagnosis of diabetes and detection of ulcer-prone regions in the feet using thermograms","volume":"38","author":"Balasenthilkumaran","year":"2022","journal-title":"Res Biomed Eng"},{"key":"10.1016\/j.diabres.2025.112446_b0235","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1166\/jno.2024.3621","article-title":"Design and numerical analysis of double encoder-swinnets-a novel swin transformers-based diabetic foot","volume":"19","author":"Maheswari","year":"2024","journal-title":"J Nanoelectron Optoelectron"},{"key":"10.1016\/j.diabres.2025.112446_b0240","doi-asserted-by":"crossref","first-page":"8511","DOI":"10.3390\/s23208511","article-title":"Diabetic plantar foot segmentation in active thermography using a two-stage adaptive gamma transform and a deep neural network","volume":"23","author":"Cao","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.diabres.2025.112446_b0245","doi-asserted-by":"crossref","first-page":"59564","DOI":"10.1109\/ACCESS.2022.3180036","article-title":"Detection of diabetes mellitus with deep learning and data augmentation techniques on foot thermography","volume":"10","author":"Anaya-Isaza","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.diabres.2025.112446_b0250","doi-asserted-by":"crossref","first-page":"3918","DOI":"10.3390\/s21113918","article-title":"Anatomical 3D modeling using IR sensors and radiometric processing based on structure from motion: towards a tool for the diabetic foot diagnosis","volume":"21","author":"Mancilla","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.diabres.2025.112446_b0255","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s12938-024-01210-6","article-title":"Protocol for metadata and image collection at diabetic foot ulcer clinics: enabling research in wound analytics and deep learning","volume":"23","author":"Basiri","year":"2024","journal-title":"Biomed Eng Online"},{"key":"10.1016\/j.diabres.2025.112446_b0260","article-title":"Quantitative analysis of pedal ulcerations using thermographic images","author":"Dhanalakshmi","year":"2023","journal-title":"Procc 2023 Int Conf Quant InfraRed Thermogr, QIRT Council"},{"key":"10.1016\/j.diabres.2025.112446_b0265","doi-asserted-by":"crossref","first-page":"5183","DOI":"10.18535\/ijetst\/v4i5.10","article-title":"Automated detection of diabetic foot using thermal images by neural network classifiers","volume":"04","author":"Purnima","year":"2017","journal-title":"Int J Emerg Trends Sci Technol"},{"key":"10.1016\/j.diabres.2025.112446_b0270","doi-asserted-by":"crossref","DOI":"10.1117\/1.JBO.20.2.026003","article-title":"Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis","volume":"20","author":"Liu","year":"2015","journal-title":"J Biomed Opt"},{"key":"10.1016\/j.diabres.2025.112446_b0275","doi-asserted-by":"crossref","DOI":"10.1016\/j.jtherbio.2022.103370","article-title":"Development of AI classification model for angiosome-wise interpretive substantiation of plantar feet thermal asymmetry in type 2 diabetic subjects using infrared thermograms","volume":"110","author":"Evangeline","year":"2022","journal-title":"J Therm Biol"},{"key":"10.1016\/j.diabres.2025.112446_b0280","doi-asserted-by":"crossref","unstructured":"Abdulshahed, College of Technical Sciences Misurata, Libya. Early Detection of Diabetes using Thermography and Artificial Neural Networks. Int J Comput Neural Eng 2017:71\u20135. https:\/\/doi.org\/10.19070\/2572-7389-170009.","DOI":"10.19070\/2572-7389-170009"},{"key":"10.1016\/j.diabres.2025.112446_b0285","doi-asserted-by":"crossref","unstructured":"Meneses-Claudio B, Huamani EL, Yauri-Machaca M. Temperature Measurement System to Detect Possible Cases of Patients with Diabetic Foot Applied to University Professors. In: Farhaoui Y, editor. Artif. Intell. Big Data IOT Block Chain Healthc. Concepts Appl., vol. 6, Cham: Springer Nature Switzerland; 2024, p. 552\u201368. https:\/\/doi.org\/10.1007\/978-3-031-65018-5_51.","DOI":"10.1007\/978-3-031-65018-5_51"},{"key":"10.1016\/j.diabres.2025.112446_b0290","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.infrared.2018.06.010","article-title":"Automated detection of diabetic foot with and without neuropathy using double density-dual tree-complex wavelet transform on foot thermograms","volume":"92","author":"Adam","year":"2018","journal-title":"Infrared Phys Technol"},{"key":"10.1016\/j.diabres.2025.112446_b0295","doi-asserted-by":"crossref","unstructured":"Soliz P, Wigdahl J, Soliz S, Saint-Lot S, Bhandarkar A, Bhatia S, et al. Computer-Based Diagnosis of Early Signs of Diabetic Peripheral Neuropathy in Thermal Videos of the Plantar Foot. 2024 IEEE Southwest Symp. Image Anal. Interpret. SSIAI, Santa Fe, NM, USA: IEEE; 2024, p. 25\u20138. https:\/\/doi.org\/10.1109\/SSIAI59505.2024.10508703.","DOI":"10.1109\/SSIAI59505.2024.10508703"},{"key":"10.1016\/j.diabres.2025.112446_b0300","doi-asserted-by":"crossref","first-page":"61","DOI":"10.54364\/cybersecurityjournal.2024.1104","article-title":"Integrated artificial intelligence approach for diabetic foot ulcer assessment: a comprehensive solution for precision diagnosis and patient care","volume":"01","author":"Al-Bahar","year":"2024","journal-title":"Adv Knowl-Based Syst Data Sci Cybersecurity"},{"key":"10.1016\/j.diabres.2025.112446_b0305","doi-asserted-by":"crossref","DOI":"10.1016\/j.infrared.2020.103187","article-title":"Automatic detection of risk zones in diabetic foot soles by processing thermographic images taken in an uncontrolled environment","volume":"105","author":"Maldonado","year":"2020","journal-title":"Infrared Phys Technol"},{"key":"10.1016\/j.diabres.2025.112446_b0310","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106407","article-title":"Affine registration of thermal images of plantar feet using convolutional neural networks","volume":"95","author":"Aferhane","year":"2024","journal-title":"Biomed Signal Process Control"},{"key":"10.1016\/j.diabres.2025.112446_b0315","article-title":"Classification method of peripheral arterial disease in patients with type 2 diabetes mellitus by infrared thermography and machine learning","volume":"111","author":"Padierna","year":"2020","journal-title":"Infrared Phys Technol"},{"key":"10.1016\/j.diabres.2025.112446_b0320","article-title":"Application of non-contact thermography as a screening modality for Diabetic Foot Syndrome \u2013 a real time cross sectional research outcome","volume":"79","author":"Evangeline","year":"2023","journal-title":"Biomed Signal Process Control"},{"key":"10.1016\/j.diabres.2025.112446_b0325","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s10483-023-2959-9","article-title":"Thermogram-based estimation of foot arterial blood flow using neural networks","volume":"44","author":"Wang","year":"2023","journal-title":"Appl Math Mech"},{"key":"10.1016\/j.diabres.2025.112446_b0330","doi-asserted-by":"crossref","DOI":"10.1111\/srt.70039","article-title":"Quantitative interpretation of infrared images of lower limbs in individuals with and without type 2 diabetes mellitus","volume":"30","author":"Fuentes-Oliver","year":"2024","journal-title":"Skin Res Technol"},{"key":"10.1016\/j.diabres.2025.112446_b0335","unstructured":"Eid MM, Yousef RN, Mohamed MA. A proposed Automated System to Classify Diabetic Foot from Thermography 2018;9."},{"key":"10.1016\/j.diabres.2025.112446_b0340","article-title":"Electronics and communications Engineering, Faculty of Engineering, Mansoura University, Egypt, Mohamed MA. Classification of Diabetic Foot thermal Images using Deep Convolutional Neural Network","volume":"8","author":"Yousef","year":"2023","journal-title":"J Intell Syst Internet Things"},{"key":"10.1016\/j.diabres.2025.112446_b0345","doi-asserted-by":"crossref","first-page":"4249","DOI":"10.3390\/s22114249","article-title":"A novel machine learning approach for severity classification of diabetic foot complications using thermogram images","volume":"22","author":"Khandakar","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.diabres.2025.112446_b0350","doi-asserted-by":"crossref","unstructured":"Bouallal D, Bougrine A, Douzi H, Harba R, Canals R, Vilcahuaman L, et al. Segmentation of plantar foot thermal images: application to diabetic foot diagnosis. 2020 Int. Conf. Syst. Signals Image Process. IWSSIP, Niter\u00f3i, Brazil: IEEE; 2020, p. 116\u201321. https:\/\/doi.org\/10.1109\/IWSSIP48289.2020.9145167.","DOI":"10.1109\/IWSSIP48289.2020.9145167"},{"key":"10.1016\/j.diabres.2025.112446_b0355","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1515\/bmt-2022-0376","article-title":"Region-wise severity analysis of diabetic plantar foot thermograms","volume":"68","author":"Sharma","year":"2023","journal-title":"Biomed Eng Biomed Tech"},{"key":"10.1016\/j.diabres.2025.112446_b0360","doi-asserted-by":"crossref","first-page":"14770","DOI":"10.1109\/JSEN.2024.3377212","article-title":"PFUTnet: a novel deep learning architecture for diabetic foot severity mapping and analysis","volume":"24","author":"Sharma","year":"2024","journal-title":"IEEE Sens J"},{"key":"10.1016\/j.diabres.2025.112446_b0365","doi-asserted-by":"crossref","first-page":"614","DOI":"10.3390\/bios14120614","article-title":"Advances in machine learning-aided thermal imaging for early detection of diabetic foot ulcers: a review","volume":"14","author":"Wu","year":"2024","journal-title":"Biosensors"},{"key":"10.1016\/j.diabres.2025.112446_b0370","doi-asserted-by":"crossref","first-page":"2896","DOI":"10.3390\/diagnostics14242896","article-title":"The challenge of deep learning for the prevention and automatic diagnosis of breast cancer: a systematic review","volume":"14","author":"P\u00e9rez-N\u00fa\u00f1ez","year":"2024","journal-title":"Diagnostics"},{"key":"10.1016\/j.diabres.2025.112446_b0375","doi-asserted-by":"crossref","first-page":"12305","DOI":"10.3390\/s140712305","article-title":"Infrared thermography for temperature measurement and non-destructive testing","volume":"14","author":"Usamentiaga","year":"2014","journal-title":"Sensors"}],"container-title":["Diabetes Research and Clinical Practice"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168822725004607?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168822725004607?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T05:16:36Z","timestamp":1758777396000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168822725004607"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":75,"alternative-id":["S0168822725004607"],"URL":"https:\/\/doi.org\/10.1016\/j.diabres.2025.112446","relation":{},"ISSN":["0168-8227"],"issn-type":[{"value":"0168-8227","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Analysis of foot thermography images of diabetic patients using artificial intelligence: a scoping review","name":"articletitle","label":"Article Title"},{"value":"Diabetes Research and Clinical Practice","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.diabres.2025.112446","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"112446"}}