{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T13:17:36Z","timestamp":1793452656683,"version":"4.5.0"},"reference-count":96,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T00:00:00Z","timestamp":1667865600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient\u2019s quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological\/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited.<\/jats:p>","DOI":"10.3390\/bios12110985","type":"journal-article","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T11:46:43Z","timestamp":1667908003000},"page":"985","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2519-381X","authenticated-orcid":false,"given":"Gaetano","family":"Chemello","sequence":"first","affiliation":[{"name":"CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benedetta","family":"Salvatori","sequence":"additional","affiliation":[{"name":"CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8327-8379","authenticated-orcid":false,"given":"Micaela","family":"Morettini","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, Via Brecce Bianche, 12, 60131 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3466-5900","authenticated-orcid":false,"given":"Andrea","family":"Tura","sequence":"additional","affiliation":[{"name":"CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,8]]},"reference":[{"key":"ref_1","first-page":"642A","article-title":"International consensus on the diabetic foot","volume":"321","author":"Newrick","year":"2000","journal-title":"BMJ"},{"key":"ref_2","unstructured":"Bowker, J.H., and Pfeifer, M.A. (2001). 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