Mining of variables from embryo morphokinetics, blastocyst’s morphology and patient parameters: An approach to predict the live birth in the assisted reproduction service

Detalhes bibliográficos
Autor(a) principal: Chéles, Dóris Spinosa [UNESP]
Data de Publicação: 2020
Outros Autores: Dal Molin, Eloiza Adriane [UNESP], Rocha, José Celso [UNESP], Nogueira, Marcelo Fábio Gouveia [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.5935/1518-0557.20200014
http://hdl.handle.net/11449/206625
Resumo: Based on growing demand for assisted reproduction technology, improved predictive models are required to optimize in vitro fertilization/intracytoplasmatic sperm injection strategies, prioritizing single embryo transfer. There are still several obstacles to overcome for the purpose of improving assisted reproductive success, such as intra-and inter-observer subjectivity in embryonic selection, high occurrence of multiple pregnancies, maternal and neonatal complications. Here, we compare studies that used several variables that impact the success of assisted reproduction, such as blastocyst morphology and morphokinetic aspects of embryo development as well as characteristics of the patients submitted to assisted reproduction, in order to predict embryo quality, implantation or live birth. Thereby, we emphasize the proposal of an artificial intelligence-based platform for a more objective method to predict live birth.
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spelling Mining of variables from embryo morphokinetics, blastocyst’s morphology and patient parameters: An approach to predict the live birth in the assisted reproduction serviceArtificial intelligenceAssisted reproductive technologyLive birth predictionBased on growing demand for assisted reproduction technology, improved predictive models are required to optimize in vitro fertilization/intracytoplasmatic sperm injection strategies, prioritizing single embryo transfer. There are still several obstacles to overcome for the purpose of improving assisted reproductive success, such as intra-and inter-observer subjectivity in embryonic selection, high occurrence of multiple pregnancies, maternal and neonatal complications. Here, we compare studies that used several variables that impact the success of assisted reproduction, such as blastocyst morphology and morphokinetic aspects of embryo development as well as characteristics of the patients submitted to assisted reproduction, in order to predict embryo quality, implantation or live birth. Thereby, we emphasize the proposal of an artificial intelligence-based platform for a more objective method to predict live birth.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Universidade Estadual PaulistaLaboratório de Matemática Aplicada Department of Biological Sciences School of Languages and Sciences São Paulo State University (UNESP), Campus AssisLaboratório de Micromanipulação Embrionária Department of Biological Sciences School of Sciences and Languages São Paulo State University (UNESP), Campus AssisLaboratório de Matemática Aplicada Department of Biological Sciences School of Languages and Sciences São Paulo State University (UNESP), Campus AssisLaboratório de Micromanipulação Embrionária Department of Biological Sciences School of Sciences and Languages São Paulo State University (UNESP), Campus AssisFAPESP: #2012/50533-2FAPESP: #2017/19323-5FAPESP: #2018/190530Universidade Estadual Paulista: #47956Universidade Estadual Paulista (Unesp)Chéles, Dóris Spinosa [UNESP]Dal Molin, Eloiza Adriane [UNESP]Rocha, José Celso [UNESP]Nogueira, Marcelo Fábio Gouveia [UNESP]2021-06-25T10:35:21Z2021-06-25T10:35:21Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article470-479http://dx.doi.org/10.5935/1518-0557.20200014Jornal Brasileiro de Reproducao Assistida, v. 24, n. 4, p. 470-479, 2020.1518-05571517-5693http://hdl.handle.net/11449/20662510.5935/1518-0557.202000142-s2.0-85092234801Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJornal Brasileiro de Reproducao Assistidainfo:eu-repo/semantics/openAccess2024-06-13T17:38:41Zoai:repositorio.unesp.br:11449/206625Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:09:37.078089Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Mining of variables from embryo morphokinetics, blastocyst’s morphology and patient parameters: An approach to predict the live birth in the assisted reproduction service
title Mining of variables from embryo morphokinetics, blastocyst’s morphology and patient parameters: An approach to predict the live birth in the assisted reproduction service
spellingShingle Mining of variables from embryo morphokinetics, blastocyst’s morphology and patient parameters: An approach to predict the live birth in the assisted reproduction service
Chéles, Dóris Spinosa [UNESP]
Artificial intelligence
Assisted reproductive technology
Live birth prediction
title_short Mining of variables from embryo morphokinetics, blastocyst’s morphology and patient parameters: An approach to predict the live birth in the assisted reproduction service
title_full Mining of variables from embryo morphokinetics, blastocyst’s morphology and patient parameters: An approach to predict the live birth in the assisted reproduction service
title_fullStr Mining of variables from embryo morphokinetics, blastocyst’s morphology and patient parameters: An approach to predict the live birth in the assisted reproduction service
title_full_unstemmed Mining of variables from embryo morphokinetics, blastocyst’s morphology and patient parameters: An approach to predict the live birth in the assisted reproduction service
title_sort Mining of variables from embryo morphokinetics, blastocyst’s morphology and patient parameters: An approach to predict the live birth in the assisted reproduction service
author Chéles, Dóris Spinosa [UNESP]
author_facet Chéles, Dóris Spinosa [UNESP]
Dal Molin, Eloiza Adriane [UNESP]
Rocha, José Celso [UNESP]
Nogueira, Marcelo Fábio Gouveia [UNESP]
author_role author
author2 Dal Molin, Eloiza Adriane [UNESP]
Rocha, José Celso [UNESP]
Nogueira, Marcelo Fábio Gouveia [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Chéles, Dóris Spinosa [UNESP]
Dal Molin, Eloiza Adriane [UNESP]
Rocha, José Celso [UNESP]
Nogueira, Marcelo Fábio Gouveia [UNESP]
dc.subject.por.fl_str_mv Artificial intelligence
Assisted reproductive technology
Live birth prediction
topic Artificial intelligence
Assisted reproductive technology
Live birth prediction
description Based on growing demand for assisted reproduction technology, improved predictive models are required to optimize in vitro fertilization/intracytoplasmatic sperm injection strategies, prioritizing single embryo transfer. There are still several obstacles to overcome for the purpose of improving assisted reproductive success, such as intra-and inter-observer subjectivity in embryonic selection, high occurrence of multiple pregnancies, maternal and neonatal complications. Here, we compare studies that used several variables that impact the success of assisted reproduction, such as blastocyst morphology and morphokinetic aspects of embryo development as well as characteristics of the patients submitted to assisted reproduction, in order to predict embryo quality, implantation or live birth. Thereby, we emphasize the proposal of an artificial intelligence-based platform for a more objective method to predict live birth.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
2021-06-25T10:35:21Z
2021-06-25T10:35:21Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.5935/1518-0557.20200014
Jornal Brasileiro de Reproducao Assistida, v. 24, n. 4, p. 470-479, 2020.
1518-0557
1517-5693
http://hdl.handle.net/11449/206625
10.5935/1518-0557.20200014
2-s2.0-85092234801
url http://dx.doi.org/10.5935/1518-0557.20200014
http://hdl.handle.net/11449/206625
identifier_str_mv Jornal Brasileiro de Reproducao Assistida, v. 24, n. 4, p. 470-479, 2020.
1518-0557
1517-5693
10.5935/1518-0557.20200014
2-s2.0-85092234801
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Jornal Brasileiro de Reproducao Assistida
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 470-479
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808129166516682752

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