| Title: | Artificial intelligence based prediction of diabetic foot risk in patients with diabetes : a literature review |
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| Authors: | ID Gosak, Lucija (Author)
ID Svenšek, Adrijana (Author)
ID Lorber, Mateja (Author)
ID Štiglic, Gregor (Author)
ID Gosak, Lucija (Copyright holder) |
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| Files: |  applsci-13-02823-v2.pdf (654,91 KB)
MD5: 0E284E987A0FBF80144DB3F5E532E094
 https://www.mdpi.com/2076-3417/13/5/2823
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| Language: | English |
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| Work type: | Unknown |
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| Typology: | 1.02 - Review Article |
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| Organization: | FZV - Faculty of Health Sciences
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| Abstract: | Diabetic foot is a prevalent chronic complication of diabetes and increases the risk of lower limb amputation, leading to both an economic and a major societal problem. By detecting the risk of developing diabetic foot sufficiently early, it can be prevented or at least postponed. Using artificial intelligence, delayed diagnosis can be prevented, leading to more intensive preventive treatment of patients. Based on a systematic literature review, we analyzed 14 articles that included the use of artificial intelligence to predict the risk of developing diabetic foot. The articles were highly heterogeneous in terms of data use and showed varying degrees of sensitivity, specificity, and accuracy. The most used machine learning techniques were support vector machine (SVM) (n = 6) and K-Nearest Neighbor (KNN) (n = 5). Future research is recommended on larger samples of participants using different techniques to determine the most effective one. |
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| Keywords: | artificial intelligence, machine learning, thermography, diabetic foot prediction, diabetes, diabetes care, diabetic foot, literature review |
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| Publication status: | Published |
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| Publication version: | Author Accepted Manuscript |
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| Publication date: | 01.01.2023 |
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| Year of publishing: | 2023 |
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| Number of pages: | str. 1-13 |
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| Numbering: | Vol. 13, iss. 5, [article no.] 2823 |
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| PID: | 20.500.12556/DKUM-86402  |
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| UDC: | 616.379-008.64:004.89 |
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| ISSN on article: | 2076-3417 |
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| COBISS.SI-ID: | 147150339 |
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| DOI: | 10.3390/app13052823 |
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| Publication date in DKUM: | 27.11.2023 |
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| Views: | 549 |
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| Downloads: | 362 |
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| Metadata: |    |
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| Categories: | Misc.
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