Face-based automatic pain assessment: challenges and perspectives in neonatal intensive care units

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dc.contributor.advisorOrcidhttps://orcid.org/0000-0001-5566-1963
dc.contributor.authorHEIDERICH, T. M.
dc.contributor.authorCARLINI, L. P.
dc.contributor.authorBUZUTI, L. F.
dc.contributor.authorBALDA, R. D. C. X.
dc.contributor.authorBARROS, M. C. M.
dc.contributor.authorGUINSBURG, R.
dc.contributor.authorCarlos E. Thomaz
dc.date.accessioned2023-08-01T06:03:19Z
dc.date.available2023-08-01T06:03:19Z
dc.date.issued2023-06-05
dc.description.abstract© 2023 Sociedade Brasileira de PediatriaObjective: To describe the challenges and perspectives of the automation of pain assessment in the Neonatal Intensive Care Unit. Data sources: A search for scientific articles published in the last 10 years on automated neonatal pain assessment was conducted in the main Databases of the Health Area and Engineering Journal Portals, using the descriptors: Pain Measurement, Newborn, Artificial Intelligence, Computer Systems, Software, Automated Facial Recognition. Summary of findings: Fifteen articles were selected and allowed a broad reflection on first, the literature search did not return the various automatic methods that exist to date, and those that exist are not effective enough to replace the human eye; second, computational methods are not yet able to automatically detect pain on partially covered faces and need to be tested during the natural movement of the neonate and with different light intensities; third, for research to advance in this area, databases are needed with more neonatal facial images available for the study of computational methods. Conclusion: There is still a gap between computational methods developed for automated neonatal pain assessment and a practical application that can be used at the bedside in real-time, that is sensitive, specific, and with good accuracy. The studies reviewed described limitations that could be minimized with the development of a tool that identifies pain by analyzing only free facial regions, and the creation and feasibility of a synthetic database of neonatal facial images that is freely available to researchers.
dc.identifier.citationHEIDERICH, T. M.; CARLINI, L. P.; BUZUTI, L. F.; BALDA, R. D. C. X.; BARROS, M. C. M.; GUINSBURG, R.; THOMAZ, C. E. Face-based automatic pain assessment: challenges and perspectives in neonatal intensive care units. Jornal de Pediatria, v. 3, n. 30, jun. 2023.
dc.identifier.doi10.1016/j.jped.2023.05.005
dc.identifier.issn1678-4782
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/4872
dc.relation.ispartofJornal de Pediatria
dc.rightsAcesso Aberto
dc.rights.licenseCreative Commons "Este é um artigo publicado em acesso aberto sob uma licença" Creative commons (CC BY 4.0). Fonte: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85162851941&origin=inward. acesso em: 10 ago. 2023.
dc.subject.otherlanguageArtificial intelligence
dc.subject.otherlanguageAutomated facial recognition
dc.subject.otherlanguageComputer systems
dc.subject.otherlanguageNewborn
dc.subject.otherlanguagePain measurement
dc.subject.otherlanguageSoftware
dc.titleFace-based automatic pain assessment: challenges and perspectives in neonatal intensive care units
dc.typeArtigo de revisão
fei.scopus.citations2
fei.scopus.eid2-s2.0-85162851941
fei.scopus.updated2024-07-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85162851941&origin=inward
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