A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images

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dc.contributor.authorRocha, José Celso [UNESP]
dc.contributor.authorPassalia, Felipe José [UNESP]
dc.contributor.authorMatos, Felipe Delestro
dc.contributor.authorTakahashi, Maria Beatriz [UNESP]
dc.contributor.authorCiniciato, Diego De Souza [UNESP]
dc.contributor.authorMaserati, Marc Peter
dc.contributor.authorAlves, Mayra Fernanda
dc.contributor.authorDe Almeida, Tamie Guibu
dc.contributor.authorCardoso, Bruna Lopes
dc.contributor.authorBasso, Andrea Cristina
dc.contributor.authorNogueira, Marcelo Fábio Gouveia [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionInstitut de Biologie de l'École Normale Supérieure de Paris
dc.contributor.institutionIn Vitro Brasil SA - Mogi Mirim
dc.date.accessioned2018-12-11T17:33:40Z
dc.date.available2018-12-11T17:33:40Z
dc.date.issued2017-12-01
dc.description.abstractMorphological analysis is the standard method of assessing embryo quality; however, its inherent subjectivity tends to generate discrepancies among evaluators. Using genetic algorithms and artificial neural networks (ANNs), we developed a new method for embryo analysis that is more robust and reliable than standard methods. Bovine blastocysts produced in vitro were classified as grade 1 (excellent or good), 2 (fair), or 3 (poor) by three experienced embryologists according to the International Embryo Technology Society (IETS) standard. The images (n = 482) were subjected to automatic feature extraction, and the results were used as input for a supervised learning process. One part of the dataset (15%) was used for a blind test posterior to the fitting, for which the system had an accuracy of 76.4%. Interestingly, when the same embryologists evaluated a sub-sample (10%) of the dataset, there was only 54.0% agreement with the standard (mode for grades). However, when using the ANN to assess this sub-sample, there was 87.5% agreement with the modal values obtained by the evaluators. The presented methodology is covered by National Institute of Industrial Property (INPI) and World Intellectual Property Organization (WIPO) patents and is currently undergoing a commercial evaluation of its feasibility.en
dc.description.affiliationUniversidade Estadual Paulista (Unesp) Faculdade de Ciências e Letras (FCL) Câmpus de Assis Laboratório de Matemática Aplicada
dc.description.affiliationInstitut de Biologie de l'École Normale Supérieure de Paris
dc.description.affiliationIn Vitro Brasil SA - Mogi Mirim
dc.description.affiliationUniversidade Estadual Paulista (Unesp) FCL Câmpus de Assis Laboratório de Micromanipulação Embrionária
dc.description.affiliationUnespUniversidade Estadual Paulista (Unesp) Faculdade de Ciências e Letras (FCL) Câmpus de Assis Laboratório de Matemática Aplicada
dc.description.affiliationUnespUniversidade Estadual Paulista (Unesp) FCL Câmpus de Assis Laboratório de Micromanipulação Embrionária
dc.identifierhttp://dx.doi.org/10.1038/s41598-017-08104-9
dc.identifier.citationScientific Reports, v. 7, n. 1, 2017.
dc.identifier.doi10.1038/s41598-017-08104-9
dc.identifier.file2-s2.0-85027160498.pdf
dc.identifier.issn2045-2322
dc.identifier.lattes3734933152414412
dc.identifier.scopus2-s2.0-85027160498
dc.identifier.urihttp://hdl.handle.net/11449/179088
dc.language.isoeng
dc.relation.ispartofScientific Reports
dc.relation.ispartofsjr1,533
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.titleA Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Imagesen
dc.typeArtigo
unesp.author.lattes3734933152414412
unesp.author.orcid0000-0002-2239-9652[11]

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Artigos - Ciências Biológicas - FCLAS