Cargando…
A computational framework to support the treatment of bedsores during COVID-19 diffusion
The treatment of pressure ulcers, also known as bedsores, is a complex process that requires to employ specialized field workforce assisting patients in their houses. In the period of COVID-19 or during any other non-trivial emergency, reaching the patients in their own house is impossible. Therefor...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9135601/ https://www.ncbi.nlm.nih.gov/pubmed/35669338 http://dx.doi.org/10.1007/s12652-022-03886-x |
_version_ | 1784713996241207296 |
---|---|
author | Di Martino, Ferdinando Orciuoli, Francesco |
author_facet | Di Martino, Ferdinando Orciuoli, Francesco |
author_sort | Di Martino, Ferdinando |
collection | PubMed |
description | The treatment of pressure ulcers, also known as bedsores, is a complex process that requires to employ specialized field workforce assisting patients in their houses. In the period of COVID-19 or during any other non-trivial emergency, reaching the patients in their own house is impossible. Therefore, as well as in the other sectors, the adoption of digital technologies is invoked to solve, or at least mitigate, the problem. In particular, during the COVID-19, the social distances should be maintained in order to decrease the risk of contagion. The Project Health Management Systems proposes a complete framework, based on Deep Learning, Augmented Reality. Pattern Matching, Image Segmentation and Edge Detection approaches, to support the treatment of bedsores without increasing the risk of contagion, i.e., improving the remote aiding of specialized operators and physicians and involving inexperienced familiars in the process. |
format | Online Article Text |
id | pubmed-9135601 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-91356012022-06-02 A computational framework to support the treatment of bedsores during COVID-19 diffusion Di Martino, Ferdinando Orciuoli, Francesco J Ambient Intell Humaniz Comput Original Research The treatment of pressure ulcers, also known as bedsores, is a complex process that requires to employ specialized field workforce assisting patients in their houses. In the period of COVID-19 or during any other non-trivial emergency, reaching the patients in their own house is impossible. Therefore, as well as in the other sectors, the adoption of digital technologies is invoked to solve, or at least mitigate, the problem. In particular, during the COVID-19, the social distances should be maintained in order to decrease the risk of contagion. The Project Health Management Systems proposes a complete framework, based on Deep Learning, Augmented Reality. Pattern Matching, Image Segmentation and Edge Detection approaches, to support the treatment of bedsores without increasing the risk of contagion, i.e., improving the remote aiding of specialized operators and physicians and involving inexperienced familiars in the process. Springer Berlin Heidelberg 2022-05-27 /pmc/articles/PMC9135601/ /pubmed/35669338 http://dx.doi.org/10.1007/s12652-022-03886-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Research Di Martino, Ferdinando Orciuoli, Francesco A computational framework to support the treatment of bedsores during COVID-19 diffusion |
title | A computational framework to support the treatment of bedsores during COVID-19 diffusion |
title_full | A computational framework to support the treatment of bedsores during COVID-19 diffusion |
title_fullStr | A computational framework to support the treatment of bedsores during COVID-19 diffusion |
title_full_unstemmed | A computational framework to support the treatment of bedsores during COVID-19 diffusion |
title_short | A computational framework to support the treatment of bedsores during COVID-19 diffusion |
title_sort | computational framework to support the treatment of bedsores during covid-19 diffusion |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9135601/ https://www.ncbi.nlm.nih.gov/pubmed/35669338 http://dx.doi.org/10.1007/s12652-022-03886-x |
work_keys_str_mv | AT dimartinoferdinando acomputationalframeworktosupportthetreatmentofbedsoresduringcovid19diffusion AT orciuolifrancesco acomputationalframeworktosupportthetreatmentofbedsoresduringcovid19diffusion AT dimartinoferdinando computationalframeworktosupportthetreatmentofbedsoresduringcovid19diffusion AT orciuolifrancesco computationalframeworktosupportthetreatmentofbedsoresduringcovid19diffusion |