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