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COVID-19 detection by means of ECG, Voice, and X-ray computerized systems: a review

dc.contributor.authorRibeiro, Pedro
dc.contributor.authorMarques, João Alexandre Lobo
dc.contributor.authorRodrigues, Pedro Miguel
dc.date.accessioned2023-03-08T15:36:34Z
dc.date.available2023-03-08T15:36:34Z
dc.date.issued2023
dc.description.abstractSince the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals’ evolution of the disease.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/bioengineering10020198pt_PT
dc.identifier.eid85149064284
dc.identifier.issn2306-5354
dc.identifier.pmcPMC9952817
dc.identifier.pmid36829692
dc.identifier.urihttp://hdl.handle.net/10400.14/40495
dc.identifier.wos000938885200001
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectComputerized diagnostic systemspt_PT
dc.subjectCOVID-19pt_PT
dc.subjectImage processingpt_PT
dc.subjectSignal processingpt_PT
dc.titleCOVID-19 detection by means of ECG, Voice, and X-ray computerized systems: a reviewpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue2pt_PT
oaire.citation.titleBioengineeringpt_PT
oaire.citation.volume10pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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