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Machine and Deep Learning Algorithms, Computer Vision Technologies, and Internet of Thingsbased Healthcare Monitoring Systems in COVID-19 Prevention, Testing, Detection, and Treatment

Machine and Deep Learning Algorithms, Computer Vision Technologies, and Internet of Thingsbased... This article reviews and advances existing literature concerning machine and deep learning algorithms, computer vision technologies, and Internet of Things-based healthcare monitoring systems in COVID-19 prevention, testing, detection, and treatment. In this research, previous findings were cumulated showing that machine learning techniques, healthcare sensor devices, and computer vision can deploy biometric data in remote COVID-19 diagnosis, and we contribute to the literature by indicating that Internet of Medical Things deploys big data analytics across embedded sensors in smart networked devices. Throughout February 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “COVID-19” + “machine and deep learning algorithms,” “computer vision technologies,” and “Internet of Thingsbased healthcare monitoring systems.” As research published between 2019 and 2022 was inspected, only 151 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 26 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, MMAT, ROBIS, and SRDR. Keywords: Internet of Things; machine and deep learning algorithm; COVID-19 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Medical Research Addleton Academic Publishers

Machine and Deep Learning Algorithms, Computer Vision Technologies, and Internet of Thingsbased Healthcare Monitoring Systems in COVID-19 Prevention, Testing, Detection, and Treatment

Katarina Zvarikova
American Journal of Medical Research , Volume 9 (1): 16 – Jan 1, 2022
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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
2334-4814
eISSN
2376-4481
Publisher site
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Abstract

This article reviews and advances existing literature concerning machine and deep learning algorithms, computer vision technologies, and Internet of Things-based healthcare monitoring systems in COVID-19 prevention, testing, detection, and treatment. In this research, previous findings were cumulated showing that machine learning techniques, healthcare sensor devices, and computer vision can deploy biometric data in remote COVID-19 diagnosis, and we contribute to the literature by indicating that Internet of Medical Things deploys big data analytics across embedded sensors in smart networked devices. Throughout February 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “COVID-19” + “machine and deep learning algorithms,” “computer vision technologies,” and “Internet of Thingsbased healthcare monitoring systems.” As research published between 2019 and 2022 was inspected, only 151 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 26 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, MMAT, ROBIS, and SRDR. Keywords: Internet of Things; machine and deep learning algorithm; COVID-19

Journal

American Journal of Medical Research – Addleton Academic Publishers

Published: Jan 1, 2022

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