| Naslov: | Artificial intelligence based prediction of diabetic foot risk in patients with diabetes : a literature review |
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| Avtorji: | ID Gosak, Lucija (Avtor)
ID Svenšek, Adrijana (Avtor)
ID Lorber, Mateja (Avtor)
ID Štiglic, Gregor (Avtor)
ID Gosak, Lucija (Lastnik avtorskih pravic) |
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| Datoteke: |  applsci-13-02823-v2.pdf (654,91 KB)
MD5: 0E284E987A0FBF80144DB3F5E532E094
 https://www.mdpi.com/2076-3417/13/5/2823
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Članek v reviji |
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| Tipologija: | 1.02 - Pregledni znanstveni članek |
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| Organizacija: | FZV - Fakulteta za zdravstvene vede
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| Opis: | 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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| Ključne besede: | artificial intelligence, machine learning, thermography, diabetic foot prediction, diabetes, diabetes care, diabetic foot, literature review |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Recenzirani rokopis |
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| Datum objave: | 01.01.2023 |
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| Leto izida: | 2023 |
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| Št. strani: | str. 1-13 |
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| Številčenje: | Vol. 13, iss. 5, [article no.] 2823 |
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| PID: | 20.500.12556/DKUM-86402  |
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| UDK: | 616.379-008.64:004.89 |
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| COBISS.SI-ID: | 147150339 |
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| DOI: | 10.3390/app13052823 |
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| ISSN pri članku: | 2076-3417 |
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| Datum objave v DKUM: | 27.11.2023 |
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| Število ogledov: | 377 |
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| Število prenosov: | 16 |
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| Metapodatki: |     |
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| Področja: | Ostalo
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