Analysis of correlation of glucose dosage by glycosimeter, laboratory dosage and artificial intelligence equipment

Detalhes bibliográficos
Autor(a) principal: Oliveira,Gabriel Garcia
Data de Publicação: 2022
Outros Autores: Barcelos,Romulo Pillon, Siqueira,Luciano de Oliveira
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Jornal Brasileiro de Patologia e Medicina Laboratorial (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1676-24442022000100102
Resumo: ABSTRACT Introduction With the increasing number of cases related to Diabetes Mellitus (DM), glycemic control through laboratory methods or rapid tests is essential. Objective To analyze the correlation of three glucose determination methodologies (Glucometer, laboratory analysis and with point of care artificial intelligence equipment). Method Blood samples from the digital pulp and venous blood from the antecubital fossa were collected from 20 volunteers of different ages and sex. Blood glucose measurements were determined by the 3 methodologies mentioned above. Result Spearmans correlation analysis carried out between all types of tests shows that there is a strong and statistically significant positive correlation, indicating the compatibility of results regardless of the method applied. Conclusion The methodologies are correlated, however, the average values?? obtained by artificial intelligence were 40% higher, which can impact the clinical interpretation of results.
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spelling Analysis of correlation of glucose dosage by glycosimeter, laboratory dosage and artificial intelligence equipmentdiabetes mellitusartificial intelligenceblood glucose self-monitoringblood glucoseABSTRACT Introduction With the increasing number of cases related to Diabetes Mellitus (DM), glycemic control through laboratory methods or rapid tests is essential. Objective To analyze the correlation of three glucose determination methodologies (Glucometer, laboratory analysis and with point of care artificial intelligence equipment). Method Blood samples from the digital pulp and venous blood from the antecubital fossa were collected from 20 volunteers of different ages and sex. Blood glucose measurements were determined by the 3 methodologies mentioned above. Result Spearmans correlation analysis carried out between all types of tests shows that there is a strong and statistically significant positive correlation, indicating the compatibility of results regardless of the method applied. Conclusion The methodologies are correlated, however, the average values?? obtained by artificial intelligence were 40% higher, which can impact the clinical interpretation of results.Sociedade Brasileira de Patologia Clínica2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1676-24442022000100102Jornal Brasileiro de Patologia e Medicina Laboratorial v.58 2022reponame:Jornal Brasileiro de Patologia e Medicina Laboratorial (Online)instname:Sociedade Brasileira de Patologia (SBP)instacron:SBP10.1900/jbpml.2022.58.414info:eu-repo/semantics/openAccessOliveira,Gabriel GarciaBarcelos,Romulo PillonSiqueira,Luciano de Oliveiraeng2022-05-26T00:00:00Zoai:scielo:S1676-24442022000100102Revistahttp://www.scielo.br/jbpmlhttps://old.scielo.br/oai/scielo-oai.php||jbpml@sbpc.org.br1678-47741676-2444opendoar:2022-05-26T00:00Jornal Brasileiro de Patologia e Medicina Laboratorial (Online) - Sociedade Brasileira de Patologia (SBP)false
dc.title.none.fl_str_mv Analysis of correlation of glucose dosage by glycosimeter, laboratory dosage and artificial intelligence equipment
title Analysis of correlation of glucose dosage by glycosimeter, laboratory dosage and artificial intelligence equipment
spellingShingle Analysis of correlation of glucose dosage by glycosimeter, laboratory dosage and artificial intelligence equipment
Oliveira,Gabriel Garcia
diabetes mellitus
artificial intelligence
blood glucose self-monitoring
blood glucose
title_short Analysis of correlation of glucose dosage by glycosimeter, laboratory dosage and artificial intelligence equipment
title_full Analysis of correlation of glucose dosage by glycosimeter, laboratory dosage and artificial intelligence equipment
title_fullStr Analysis of correlation of glucose dosage by glycosimeter, laboratory dosage and artificial intelligence equipment
title_full_unstemmed Analysis of correlation of glucose dosage by glycosimeter, laboratory dosage and artificial intelligence equipment
title_sort Analysis of correlation of glucose dosage by glycosimeter, laboratory dosage and artificial intelligence equipment
author Oliveira,Gabriel Garcia
author_facet Oliveira,Gabriel Garcia
Barcelos,Romulo Pillon
Siqueira,Luciano de Oliveira
author_role author
author2 Barcelos,Romulo Pillon
Siqueira,Luciano de Oliveira
author2_role author
author
dc.contributor.author.fl_str_mv Oliveira,Gabriel Garcia
Barcelos,Romulo Pillon
Siqueira,Luciano de Oliveira
dc.subject.por.fl_str_mv diabetes mellitus
artificial intelligence
blood glucose self-monitoring
blood glucose
topic diabetes mellitus
artificial intelligence
blood glucose self-monitoring
blood glucose
description ABSTRACT Introduction With the increasing number of cases related to Diabetes Mellitus (DM), glycemic control through laboratory methods or rapid tests is essential. Objective To analyze the correlation of three glucose determination methodologies (Glucometer, laboratory analysis and with point of care artificial intelligence equipment). Method Blood samples from the digital pulp and venous blood from the antecubital fossa were collected from 20 volunteers of different ages and sex. Blood glucose measurements were determined by the 3 methodologies mentioned above. Result Spearmans correlation analysis carried out between all types of tests shows that there is a strong and statistically significant positive correlation, indicating the compatibility of results regardless of the method applied. Conclusion The methodologies are correlated, however, the average values?? obtained by artificial intelligence were 40% higher, which can impact the clinical interpretation of results.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1676-24442022000100102
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1676-24442022000100102
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1900/jbpml.2022.58.414
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv
Sociedade Brasileira de Patologia Clínica
publisher.none.fl_str_mv
Sociedade Brasileira de Patologia Clínica
dc.source.none.fl_str_mv Jornal Brasileiro de Patologia e Medicina Laboratorial v.58 2022
reponame:Jornal Brasileiro de Patologia e Medicina Laboratorial (Online)
instname:Sociedade Brasileira de Patologia (SBP)
instacron:SBP
instname_str Sociedade Brasileira de Patologia (SBP)
instacron_str SBP
institution SBP
reponame_str Jornal Brasileiro de Patologia e Medicina Laboratorial (Online)
collection Jornal Brasileiro de Patologia e Medicina Laboratorial (Online)
repository.name.fl_str_mv Jornal Brasileiro de Patologia e Medicina Laboratorial (Online) - Sociedade Brasileira de Patologia (SBP)
repository.mail.fl_str_mv ||jbpml@sbpc.org.br
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