{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T21:22:08Z","timestamp":1793481728763,"version":"4.5.0"},"reference-count":22,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T00:00:00Z","timestamp":1754179200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Diabetes Sci Technol"],"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Diabetic foot ulcers (DFUs) affect 19% to 34% of individuals with diabetes during their lifetime and account for over one million nontraumatic lower-limb amputations annually. Standard care often fails to detect early, subclinical changes, leading to delayed intervention and high mortality rates. This review examines how artificial intelligence (AI) and machine learning (ML) can extract complex patterns from diverse data modalities to advance DFU care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We examined AI\/ML applications in DFU care across four domains: diagnosis (automated image and thermogram classification, biomechanical risk stratification), treatment optimization (AI-driven offloading prescriptions, tele-rehabilitation, molecularly informed wound care), prognosis (neural network and random forest models for risk assessment), and novel strategy development (generative AI, transcriptomic target discovery, wearable digital biomarkers).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Artificial intelligence\/ML methodologies have demonstrated promising results in DFU image and thermogram analysis, with reported accuracies ranging from 81-97% across different studies. Biomechanical ML models show potential for dynamic risk stratification, and prognostic models achieve moderate performance with area under the curve values around 0.74-0.82. Generative AI approaches have shown promise for data augmentation, improving segmentation performance in limited datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Despite promising advances, several challenges impede clinical translation, including data standardization, model explainability, regulatory compliance, clinical workflow integration, prospective validation, and equitable implementation. Collaborative efforts among clinicians, data scientists, regulators, and patients are essential to translate AI-driven innovations into routine DFU management, potentially reducing amputations and improving outcomes for this global health burden.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/19322968251363632","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T05:20:32Z","timestamp":1754284832000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":18,"title":["Artificial Intelligence and Machine Learning in Diabetic Foot Ulcer Care: Advances in Diagnosis, Treatment, Prognosis, and Novel Therapeutic Strategies"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5270-1429","authenticated-orcid":false,"given":"Abdulhamit","family":"Misir","sequence":"first","affiliation":[{"name":"Department of Orthopedics and Traumatology, Bah\u00e7e\u015fehir University Faculty of Medicine, Medicalpark Goztepe Hospital, Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare13060648"},{"key":"e_1_3_2_3_2","unstructured":"International Working Group on the Diabetic Foot. IWGDF guidelines on the prevention and management of diabetic foot disease 2023. https:\/\/iwgdfguidelines.org\/wp-content\/uploads\/2023\/07\/IWGDF-Guidelines-2023.pdf. Accessed July 23 2025."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1111\/iwj.12404"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtv.2024.07.004"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/bios12110985"},{"key":"e_1_3_2_7_2","first-page":"1","volume-title":"Diabetic Foot Ulcers\u2014Pathogenesis, Innovative Treatments and AI Applications","author":"Ahmed SA","year":"2023","unstructured":"Ahmed SA, Kabir MA, Chowdhury MEH, Nancarrow S. AI-driven personalized offloading device prescriptions: a cutting-edge approach to preventing diabetes-related plantar forefoot ulcers and complications. In: Chowdhury MEH, Nancarrow S, Kabir MA, eds. Diabetic Foot Ulcers\u2014Pathogenesis, Innovative Treatments and AI Applications. London, England: Intechopen; 2023:1-15."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14161807"},{"issue":"3","key":"e_1_3_2_9_2","first-page":"350","article-title":"Plantar thermogram analysis using deep learning for diabetic foot risk classification","volume":"19","author":"Panamonta V","year":"2025","unstructured":"Panamonta V, Jerawatana R, Ariyaprayoon P, et al. Plantar thermogram analysis using deep learning for diabetic foot risk classification. J Diabetes Sci Technol. 2025;19(3):350-354.","journal-title":"J Diabetes Sci Technol"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-024-10267-3"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22114249"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12938-024-01210-6"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3425907"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbiomech.2021.110699"},{"key":"e_1_3_2_15_2","first-page":"323","article-title":"Machine learning approach to diabetic foot risk classification with biomechanics data","volume":"98","author":"Guiotto A","year":"2022","unstructured":"Guiotto A, Bortolami G, Ciniglio A, et al. Machine learning approach to diabetic foot risk classification with biomechanics data. Gait Posture. 2022;98:323-330.","journal-title":"Gait Posture"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.3390\/technologies12110231"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbiomech.2016.10.033"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22093507"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22239278"},{"issue":"4","key":"e_1_3_2_20_2","first-page":"567","article-title":"Research progress on risk prediction models for the diabetic foot","volume":"62","author":"Qi H","year":"2025","unstructured":"Qi H, Zhang T, Hou L, Li Q, Huang R, Ma L. Research progress on risk prediction models for the diabetic foot. Acta Diabetol. 2025;62(4):567-580.","journal-title":"Acta Diabetol"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1186\/s10020-024-00955-z"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12185816"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.3390\/medicina60101718"}],"container-title":["Journal of Diabetes Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/19322968251363632","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/19322968251363632","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/19322968251363632","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T18:18:05Z","timestamp":1777400285000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/19322968251363632"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":22,"alternative-id":["10.1177\/19322968251363632"],"URL":"https:\/\/doi.org\/10.1177\/19322968251363632","relation":{},"ISSN":["1932-2968","1932-2968"],"issn-type":[{"value":"1932-2968","type":"print"},{"value":"1932-2968","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,3]]},"article-number":"19322968251363632"}}