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A step toward better sample management of COVID-19: On-spot detection by biometric technology and artificial intelligence
The outbreak of the corona virus pandemic has severely disrupted human lives and socioeconomical structures across most nations around the world since December 2019. Researchers around the world are working on finding solutions to overcome or at least control the pandemic. Biometric monitoring and a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334987/ http://dx.doi.org/10.1016/B978-0-323-91307-2.00017-1 |
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author | Sharma, Vivek Dastidar, Monalisha Ghosh Sutradhar, Sarada Raj, Veena De Silva, Kithma Roy, Sharmili |
author_facet | Sharma, Vivek Dastidar, Monalisha Ghosh Sutradhar, Sarada Raj, Veena De Silva, Kithma Roy, Sharmili |
author_sort | Sharma, Vivek |
collection | PubMed |
description | The outbreak of the corona virus pandemic has severely disrupted human lives and socioeconomical structures across most nations around the world since December 2019. Researchers around the world are working on finding solutions to overcome or at least control the pandemic. Biometric monitoring and artificial intelligence (AI) offer on-spot sample management for monitoring patients as advanced applications of modern technologies. Various telehealth and remote monitoring systems have become prevalent for early detection of symptoms by collecting dynamic data from patients through biometric monitoring technologies (BMTs). In this chapter, we have summarized rapid diagnostic kits that are available in the market recently. Furthermore, the principle of BMTs and its applications have been discussed in this chapter. In addition, the benefits and challenges involved in using these advanced technologies have also been summarized. Further, advantages of AI technologies that are used in proper screening, analysis, tracking, and prediction have been discussed. We also provided a detailed insight on systematic sample handling processes using the above-mentioned technologies. The advantages of remote data collection and future outlooks of biosensors to be employed remotely for COVID-19 detection and diagnosis have also been explored. |
format | Online Article Text |
id | pubmed-9334987 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-93349872022-07-29 A step toward better sample management of COVID-19: On-spot detection by biometric technology and artificial intelligence Sharma, Vivek Dastidar, Monalisha Ghosh Sutradhar, Sarada Raj, Veena De Silva, Kithma Roy, Sharmili COVID-19 and the Sustainable Development Goals Article The outbreak of the corona virus pandemic has severely disrupted human lives and socioeconomical structures across most nations around the world since December 2019. Researchers around the world are working on finding solutions to overcome or at least control the pandemic. Biometric monitoring and artificial intelligence (AI) offer on-spot sample management for monitoring patients as advanced applications of modern technologies. Various telehealth and remote monitoring systems have become prevalent for early detection of symptoms by collecting dynamic data from patients through biometric monitoring technologies (BMTs). In this chapter, we have summarized rapid diagnostic kits that are available in the market recently. Furthermore, the principle of BMTs and its applications have been discussed in this chapter. In addition, the benefits and challenges involved in using these advanced technologies have also been summarized. Further, advantages of AI technologies that are used in proper screening, analysis, tracking, and prediction have been discussed. We also provided a detailed insight on systematic sample handling processes using the above-mentioned technologies. The advantages of remote data collection and future outlooks of biosensors to be employed remotely for COVID-19 detection and diagnosis have also been explored. 2022 2022-07-29 /pmc/articles/PMC9334987/ http://dx.doi.org/10.1016/B978-0-323-91307-2.00017-1 Text en Copyright © 2022 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Sharma, Vivek Dastidar, Monalisha Ghosh Sutradhar, Sarada Raj, Veena De Silva, Kithma Roy, Sharmili A step toward better sample management of COVID-19: On-spot detection by biometric technology and artificial intelligence |
title | A step toward better sample management of COVID-19: On-spot detection by biometric technology and artificial intelligence |
title_full | A step toward better sample management of COVID-19: On-spot detection by biometric technology and artificial intelligence |
title_fullStr | A step toward better sample management of COVID-19: On-spot detection by biometric technology and artificial intelligence |
title_full_unstemmed | A step toward better sample management of COVID-19: On-spot detection by biometric technology and artificial intelligence |
title_short | A step toward better sample management of COVID-19: On-spot detection by biometric technology and artificial intelligence |
title_sort | step toward better sample management of covid-19: on-spot detection by biometric technology and artificial intelligence |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334987/ http://dx.doi.org/10.1016/B978-0-323-91307-2.00017-1 |
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