COVID-19 Control by Computer Vision Approaches: A Survey

Ulhaq, Anwaar; Born, Jannis; Khan, Asim; Gomes, Douglas Pinto Sampaio; Chakraborty, Subrata; Paul, Manoranjan

doi:10.1109/access.2020.3027685

Cited by 125 publications

(63 citation statements)

References 98 publications

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“…After removing duplicates, we screened 725 studies, of which 33 were read in the form of full texts. In total, we included 22 reviews [46–67] for qualitative synthesis. Our reporting is consistent with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines [68].…”

Section: Resultsmentioning

confidence: 99%

A Call For Better Methodological Quality Of Reviews On Using Artificial Intelligence For COVID-19 Detection In Medical Imaging – An Umbrella Systematic Review

Jemioło

Storman

Orzechowski

2021

Preprint

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Objective: In this umbrella systematic review, we screen existing reviews on using artificial intelligence(AI) techniques to diagnose COVID-19 in patients of any age and sex (both hospitalised and ambulatory) using medical images and assess their methodological quality. Methods: We searched seven databases (MEDLINE, EMBASE, Web of Science, Scopus, dblp, CochraneLibrary, IEEE Xplore) and two preprint services (arXiv, OSF Preprints) up to September 1, 2020. Eligible studies were identified as reviews or surveys where any metric of classification of detection of COVID-19 using AI was provided. Two independent reviewers did all steps of identification of records (titles and abstracts screening, full texts assessment, essential data extraction, and quality assessment). Any discrepancies were resolved by discussion. We qualitatively analyse methodological credibility ofthe reviews using AMSTAR 2 and evaluate reporting using PRISMA-DTA tools, leaving quantitative analysis for further publications. Results: We included 22 reviews out of 725 records covering 165 primary studies. This review covers 416,254 participants in total, including 50,022 diagnosed with COVID-19. The methodological quality of all eligible studies was rated as critically low. 91% of papers had significant flaws in reporting quality. More than half of the reviews did not comment on the results of previously published reviews at all. Almost three fourth of the studies included less than 10% of available studies. Discussion: In this umbrella review, we focus on the descriptive summary of included papers. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Due to the low credibility of evidence and flawed reporting, any recommendation about automated COVID-19 clinical diagnosis from medical images using AI at this point cannot be provided. Funding: PO was supported by NIH grant AI116794 (the funding body had no role in the design, in any stage of the review, or in writing the manuscript); PJ and DS did not receive any funding. Registration:The protocol of this review was registered on the OSF platform at osf.io/kxrmh

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Section: Resultsmentioning

confidence: 99%

A Call For Better Methodological Quality Of Reviews On Using Artificial Intelligence For COVID-19 Detection In Medical Imaging – An Umbrella Systematic Review

Jemioło

Storman

Orzechowski

2021

Preprint

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“…The study provided insight into how objective lung disease diagnosis can be made in small data environments. Ulhaq et al ( 2020 ) analyzed the impact of COVID-19 in the market and propose a solution by computer vision. The author collects the data from various sources like the WHO’s official website and used various research tools to analyze.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients

2021

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“…For example, body temperature monitoring can be applied to early detection strategy for infected people. 17,18 Internet of Things (IoT)…”

Section: Covid-19mentioning

confidence: 99%

Apply IOT technology to practice a pandemic prevention body temperature measurement system: A case study of response measures for COVID-19

Lin

Hsieh

Chen

et al. 2021

International Journal of Distributed Sensor Networks

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Today, the most serious threat to global health is the continuous outbreak of respiratory diseases, which is called Coronavirus Disease 2019 (COVID-19). The outbreak of COVID-19 has brought severe challenges to public health and has attracted great attention from the research and medical communities. Most patients infected with COVID-19 will have fever. Therefore, the monitoring of body temperature has become one of the most important basis for pandemic prevention and testing. Among them, the measurement of body temperature is the most direct through the Forehead Thermometer, but the measurement speed is relatively slow. The cost of fast-checking body temperature measurement equipment, such as infrared body temperature detection and face recognition temperature machine, is too high, and it is difficult to build Disease Surveillance System (DSS). To solve the above-mentioned problems, the Intelligent pandemic prevention Temperature Measurement System (ITMS) and Pandemic Prevention situation Analysis System (PPAS) are proposed in this study. ITMS is used to detect body temperature. However, PPAS uses big data analysis techniques to prevent pandemics. In this study, the campus field is used as an example, in which ITMS and PPAS are used. In the research, Proof of Concept (PoC), Proof of Service (PoS), and Proof of Business (PoB) were carried out for the use of ITMS and PPAS in the campus area. From the verification, it can be seen that ITMS and PPAS can be successfully used in campus fields and are widely recognized by users. Through the verification of this research, it can be determined that ITMS and PPAS are indeed feasible and capable of dissemination. The ITMS and PPAS are expected to give full play to their functions during the spread of pandemics. All in all, the results of this research will provide a wide range of applied thinking for people who are committed to the development of science and technology.

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COVID-19 Control by Computer Vision Approaches: A Survey

Cited by 125 publications

References 98 publications

A Call For Better Methodological Quality Of Reviews On Using Artificial Intelligence For COVID-19 Detection In Medical Imaging – An Umbrella Systematic Review

A Call For Better Methodological Quality Of Reviews On Using Artificial Intelligence For COVID-19 Detection In Medical Imaging – An Umbrella Systematic Review

A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients

Apply IOT technology to practice a pandemic prevention body temperature measurement system: A case study of response measures for COVID-19

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