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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 Things-based 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

How to cite: Zvarikova, K., Horak, J., and Bradley, P. (2022). “Machine and Deep Learning Algorithms, Computer Vision Technologies, and Internet of Things-based Healthcare Monitoring Systems in COVID-19 Prevention, Testing, Detection, and Treatment,” American Journal of Medical Research 9(1): 145–160. doi: 10.22381/ajmr91202210.

Received 27 February 2022 • Received in revised form 22 April 2022
Accepted 26 April 2022 • Available online 30 April 2022

1Faculty of Operation and Economics of Transport and Communications, Department of Economics, University of Zilina, Slovak Republic, This email address is being protected from spambots. You need JavaScript enabled to view it..
2The School of Expertness and Valuation, The Institute of Technology and Business in Ceske Budejovice, Czech Republic, This email address is being protected from spambots. You need JavaScript enabled to view it..
3Digital Epidemiological Surveillance Research Unit at ISBDA, Louisville, KY, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.. (corresponding author)

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