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A review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring
Diabetic foot complications have multiple adverse effects in a person’s quality of life. Yet, efficient monitoring schemes can mitigate or postpone any disorders, mainly by early detecting regions of interest. Nowadays, optical sensors and artificial intelligence (AI) tools can contribute efficientl...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9635839/ https://www.ncbi.nlm.nih.gov/pubmed/36338484 http://dx.doi.org/10.3389/fphys.2022.924546 |
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author | Kaselimi, Maria Protopapadakis, Eftychios Doulamis, Anastasios Doulamis, Nikolaos |
author_facet | Kaselimi, Maria Protopapadakis, Eftychios Doulamis, Anastasios Doulamis, Nikolaos |
author_sort | Kaselimi, Maria |
collection | PubMed |
description | Diabetic foot complications have multiple adverse effects in a person’s quality of life. Yet, efficient monitoring schemes can mitigate or postpone any disorders, mainly by early detecting regions of interest. Nowadays, optical sensors and artificial intelligence (AI) tools can contribute efficiently to such monitoring processes. In this work, we provide information on the adopted imaging schemes and related optical sensors on this topic. The analysis considers both the physiology of the patients and the characteristics of the sensors. Currently, there are multiple approaches considering both visible and infrared bands (multiple ranges), most of them coupled with various AI tools. The source of the data (sensor type) can support different monitoring strategies and imposes restrictions on the AI tools that should be used with. This review provides a comprehensive literature review of AI-assisted DFU monitoring methods. The paper presents the outcomes of a large number of recently published scholarly articles. Furthermore, the paper discusses the highlights of these methods and the challenges for transferring these methods into a practical and trustworthy framework for sufficient remote management of the patients. |
format | Online Article Text |
id | pubmed-9635839 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96358392022-11-05 A review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring Kaselimi, Maria Protopapadakis, Eftychios Doulamis, Anastasios Doulamis, Nikolaos Front Physiol Physiology Diabetic foot complications have multiple adverse effects in a person’s quality of life. Yet, efficient monitoring schemes can mitigate or postpone any disorders, mainly by early detecting regions of interest. Nowadays, optical sensors and artificial intelligence (AI) tools can contribute efficiently to such monitoring processes. In this work, we provide information on the adopted imaging schemes and related optical sensors on this topic. The analysis considers both the physiology of the patients and the characteristics of the sensors. Currently, there are multiple approaches considering both visible and infrared bands (multiple ranges), most of them coupled with various AI tools. The source of the data (sensor type) can support different monitoring strategies and imposes restrictions on the AI tools that should be used with. This review provides a comprehensive literature review of AI-assisted DFU monitoring methods. The paper presents the outcomes of a large number of recently published scholarly articles. Furthermore, the paper discusses the highlights of these methods and the challenges for transferring these methods into a practical and trustworthy framework for sufficient remote management of the patients. Frontiers Media S.A. 2022-10-21 /pmc/articles/PMC9635839/ /pubmed/36338484 http://dx.doi.org/10.3389/fphys.2022.924546 Text en Copyright © 2022 Kaselimi, Protopapadakis, Doulamis and Doulamis. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Kaselimi, Maria Protopapadakis, Eftychios Doulamis, Anastasios Doulamis, Nikolaos A review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring |
title | A review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring |
title_full | A review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring |
title_fullStr | A review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring |
title_full_unstemmed | A review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring |
title_short | A review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring |
title_sort | review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9635839/ https://www.ncbi.nlm.nih.gov/pubmed/36338484 http://dx.doi.org/10.3389/fphys.2022.924546 |
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