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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...

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Autores principales: Kaselimi, Maria, Protopapadakis, Eftychios, Doulamis, Anastasios, Doulamis, Nikolaos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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