Evidence map›Paper›PMID 33810086›Full record

ReviewInternational journal of environmental research and public health2021

An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies.

David Perpetuini, Chiara Filippini, Daniela Cardone, Arcangelo Merla

Abstract readReview
In one paragraph

Review in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
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

25 citing papers in PubMed.

  1. Article
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  6. Article
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  11. Article
  12. Can Adipose Tissue Influence the Evaluation of Thermographic Images in Adolescents?International journal of environmental research and public health · 2023
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. COVID-19 classification using thermal images.Journal of biomedical optics · 2022
    Article
  18. Article
  19. Article
  20. 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

4 authors.

David PerpetuiniDepartment of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University G. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy.ORCID 0000-0003-1903-0501
Chiara FilippiniDepartment of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University G. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy.ORCID 0000-0003-2282-3537
Daniela CardoneDepartment of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University G. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy.ORCID 0000-0002-1506-1995
Arcangelo MerlaDepartment of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University G. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Infrared thermal imaging (IRI) is a contact-less technology able to monitor human skin temperature for biomedical applications and in real-life contexts. Its capacity to detect fever was exploited for mass screening during past epidemic emergencies as well as for the current COVID-19 pandemic. However, the only assessment of fever may not be selective for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Hence, novel approaches for IRI data analysis have been investigated. The present review aims to describe how IRI have been employed during the last epidemics, highlighting the potentialities and the limitations of this technology to contain the contagions. Specifically, the methods employed for automatic face recognition and fever assessment and IRI's performances in mass screening at airports and hospitals are reviewed. Moreover, an overview of novel machine learning methods for IRI data analysis, aimed to identify respiratory diseases, is provided. In addition, IRI-based smart technologies developed to support the healthcare during the COVID-19 pandemic are described. Finally, relevant guidelines to fully exploit IRI for COVID-19 identification are defined, to improve the effectiveness of IRI in the detection of the SARS-CoV-2 infection.

Indexed as

COVID-19PandemicsEmergenciesFeverHumansSARS-CoV-2artificial intelligenceCOVID-19face recognitionfever detectionmachine learningpandemicSARS-CoV-2thermography

Identifiers

PMID33810086
PMCPMC8004954

What OpenQuestion holds

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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.