Evidence map›Paper›PMID 36099862›Full record

ReviewComputers in biology and medicine2022

A comprehensive review of COVID-19 detection techniques: From laboratory systems to wearable devices.

Khalid Alyafei, Rashid Ahmed, Farhan Fuad Abir, Muhammad E H Chowdhury, Khalid Kamal Naji

Open access · greenAbstract readReview
In one paragraph

Review in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.4field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 2 countries.

Khalid AlyafeiDepartment of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha, 2713, Qatar.
Rashid AhmedDepartment of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha, 2713, Qatar; Department of Biotechnology, Mirpur University of Science and Technology (MUST), Mirpur, 10250, AJK, Pakistan.
Farhan Fuad AbirDepartment of Electrical Engineering, Qatar University, Doha, 2713, Qatar.
Muhammad E H ChowdhuryDepartment of Electrical Engineering, Qatar University, Doha, 2713, Qatar. Electronic address: mchowdhury@qu.edu.qa.
Khalid Kamal NajiCollege of Engineering, Qatar University, Doha, 2713, Qatar. Electronic address: knaji@qu.edu.qa.
Qatar University · QA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Screening of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among symptomatic and asymptomatic patients offers unique opportunities for curtailing the transmission of novel coronavirus disease 2019, commonly known as COVID-19. Molecular diagnostic techniques, namely reverse transcription loop-mediated isothermal amplification (RT-LAMP), reverse transcription-polymerase chain reaction (RT-PCR), and immunoassays, have been frequently used to identify COVID-19 infection. Although these techniques are robust and accurate, mass testing of potentially infected individuals has shown difficulty due to the resources, manpower, and costs it entails. Moreover, as these techniques are typically used to test symptomatic patients, healthcare systems have failed to screen asymptomatic patients, whereas the spread of COVID-19 by these asymptomatic individuals has turned into a crucial problem. Besides, respiratory infections or cardiovascular conditions generally demonstrate changes in physiological parameters, namely body temperature, blood pressure, and breathing rate, which signifies the onset of diseases. Such vitals monitoring systems have shown promising results employing artificial intelligence (AI). Therefore, the potential use of wearable devices for monitoring asymptomatic COVID-19 individuals has recently been explored. This work summarizes the efforts that have been made in the domains from laboratory-based testing to asymptomatic patient monitoring via wearable systems.

Indexed as

COVID-19Wearable Electronic DevicesArtificial IntelligenceClinical Laboratory TechniquesCOVID-19 TestingHumansSARS-CoV-2Sensitivity and SpecificityAsymptomaticCOVID-19Machine learningScreeningWearable systems

Identifiers

PMID36099862
PMCPMC9433350
OpenAlexW4294325847

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.