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

Abstract Morphological 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 a...

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Autores principales:	José Celso Rocha, Felipe José Passalia, Felipe Delestro Matos, Maria Beatriz Takahashi, Diego de Souza Ciniciato, Marc Peter Maserati, Mayra Fernanda Alves, Tamie Guibu de Almeida, Bruna Lopes Cardoso, Andrea Cristina Basso, Marcelo Fábio Gouveia Nogueira
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2fe988d99301483d8ada2e554365d672
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spelling	oai:doaj.org-article:2fe988d99301483d8ada2e554365d6722021-12-02T15:05:15ZA Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images10.1038/s41598-017-08104-92045-2322https://doaj.org/article/2fe988d99301483d8ada2e554365d6722017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08104-9https://doaj.org/toc/2045-2322Abstract Morphological 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.José Celso RochaFelipe José PassaliaFelipe Delestro MatosMaria Beatriz TakahashiDiego de Souza CiniciatoMarc Peter MaseratiMayra Fernanda AlvesTamie Guibu de AlmeidaBruna Lopes CardosoAndrea Cristina BassoMarcelo Fábio Gouveia NogueiraNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q José Celso Rocha Felipe José Passalia Felipe Delestro Matos Maria Beatriz Takahashi Diego de Souza Ciniciato Marc Peter Maserati Mayra Fernanda Alves Tamie Guibu de Almeida Bruna Lopes Cardoso Andrea Cristina Basso Marcelo Fábio Gouveia Nogueira A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images
description	Abstract Morphological 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.
format	article
author	José Celso Rocha Felipe José Passalia Felipe Delestro Matos Maria Beatriz Takahashi Diego de Souza Ciniciato Marc Peter Maserati Mayra Fernanda Alves Tamie Guibu de Almeida Bruna Lopes Cardoso Andrea Cristina Basso Marcelo Fábio Gouveia Nogueira
author_facet	José Celso Rocha Felipe José Passalia Felipe Delestro Matos Maria Beatriz Takahashi Diego de Souza Ciniciato Marc Peter Maserati Mayra Fernanda Alves Tamie Guibu de Almeida Bruna Lopes Cardoso Andrea Cristina Basso Marcelo Fábio Gouveia Nogueira
author_sort	José Celso Rocha
title	A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images
title_short	A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images
title_full	A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images
title_fullStr	A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images
title_full_unstemmed	A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images
title_sort	method based on artificial intelligence to fully automatize the evaluation of bovine blastocyst images
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/2fe988d99301483d8ada2e554365d672
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A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images

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