Bayesian classification for the selection of in vitro human embryos using morphological and clinical data
Abstract In vitro fertilization (IVF) is a medically assisted reproduction technique that enables infertile couples to achieve successful pregnancy. Given the uncertainty of the treatment, we propose an intelligent decision support system based on supervised classification by Bayesian classifiers to...
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Veröffentlicht in: | Computer methods and programs in biomedicine 2008-05, Vol.90 (2), p.104-116 |
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creator | Morales, Dinora Araceli Bengoetxea, Endika Larrañaga, Pedro García, Miguel Franco, Yosu Fresnada, Mónica Merino, Marisa |
description | Abstract In vitro fertilization (IVF) is a medically assisted reproduction technique that enables infertile couples to achieve successful pregnancy. Given the uncertainty of the treatment, we propose an intelligent decision support system based on supervised classification by Bayesian classifiers to aid to the selection of the most promising embryos that will form the batch to be transferred to the woman’s uterus. The aim of the supervised classification system is to improve overall success rate of each IVF treatment in which a batch of embryos is transferred each time, where the success is achieved when implantation (i.e. pregnancy) is obtained. Due to ethical reasons, different legislative restrictions apply in every country on this technique. In Spain, legislation allows a maximum of three embryos to form each transfer batch. As a result, clinicians prefer to select the embryos by non-invasive embryo examination based on simple methods and observation focused on morphology and dynamics of embryo development after fertilization. This paper proposes the application of Bayesian classifiers to this embryo selection problem in order to provide a decision support system that allows a more accurate selection than with the actual procedures which fully rely on the expertise and experience of embryologists. For this, we propose to take into consideration a reduced subset of feature variables related to embryo morphology and clinical data of patients, and from this data to induce Bayesian classification models. Results obtained applying a filter technique to choose the subset of variables, and the performance of Bayesian classifiers using them, are presented. |
doi_str_mv | 10.1016/j.cmpb.2007.11.018 |
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Given the uncertainty of the treatment, we propose an intelligent decision support system based on supervised classification by Bayesian classifiers to aid to the selection of the most promising embryos that will form the batch to be transferred to the woman’s uterus. The aim of the supervised classification system is to improve overall success rate of each IVF treatment in which a batch of embryos is transferred each time, where the success is achieved when implantation (i.e. pregnancy) is obtained. Due to ethical reasons, different legislative restrictions apply in every country on this technique. In Spain, legislation allows a maximum of three embryos to form each transfer batch. As a result, clinicians prefer to select the embryos by non-invasive embryo examination based on simple methods and observation focused on morphology and dynamics of embryo development after fertilization. This paper proposes the application of Bayesian classifiers to this embryo selection problem in order to provide a decision support system that allows a more accurate selection than with the actual procedures which fully rely on the expertise and experience of embryologists. For this, we propose to take into consideration a reduced subset of feature variables related to embryo morphology and clinical data of patients, and from this data to induce Bayesian classification models. Results obtained applying a filter technique to choose the subset of variables, and the performance of Bayesian classifiers using them, are presented.</description><identifier>ISSN: 0169-2607</identifier><identifier>EISSN: 1872-7565</identifier><identifier>DOI: 10.1016/j.cmpb.2007.11.018</identifier><identifier>PMID: 18190996</identifier><language>eng</language><publisher>Ireland: Elsevier Ireland Ltd</publisher><subject>Artificial Intelligence ; Bayes Theorem ; Bayesian classifiers ; Decision support systems ; Decision Support Techniques ; Embryo Transfer - methods ; Embryo Transfer - statistics & numerical data ; Feature subset selection ; Female ; Fertilization in Vitro - methods ; Fertilization in Vitro - statistics & numerical data ; Humans ; Internal Medicine ; IVF outcome prediction ; Male ; Other ; Pregnancy ; Zygote - classification ; Zygote - ultrastructure</subject><ispartof>Computer methods and programs in biomedicine, 2008-05, Vol.90 (2), p.104-116</ispartof><rights>Elsevier Ireland Ltd</rights><rights>2007 Elsevier Ireland Ltd</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4658-4e961ce44b40517cf79f3d709dd5ec97ddfa32c21c878b62527df9726b6429be3</citedby><cites>FETCH-LOGICAL-c4658-4e961ce44b40517cf79f3d709dd5ec97ddfa32c21c878b62527df9726b6429be3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.cmpb.2007.11.018$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/18190996$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Morales, Dinora Araceli</creatorcontrib><creatorcontrib>Bengoetxea, Endika</creatorcontrib><creatorcontrib>Larrañaga, Pedro</creatorcontrib><creatorcontrib>García, Miguel</creatorcontrib><creatorcontrib>Franco, Yosu</creatorcontrib><creatorcontrib>Fresnada, Mónica</creatorcontrib><creatorcontrib>Merino, Marisa</creatorcontrib><title>Bayesian classification for the selection of in vitro human embryos using morphological and clinical data</title><title>Computer methods and programs in biomedicine</title><addtitle>Comput Methods Programs Biomed</addtitle><description>Abstract In vitro fertilization (IVF) is a medically assisted reproduction technique that enables infertile couples to achieve successful pregnancy. Given the uncertainty of the treatment, we propose an intelligent decision support system based on supervised classification by Bayesian classifiers to aid to the selection of the most promising embryos that will form the batch to be transferred to the woman’s uterus. The aim of the supervised classification system is to improve overall success rate of each IVF treatment in which a batch of embryos is transferred each time, where the success is achieved when implantation (i.e. pregnancy) is obtained. Due to ethical reasons, different legislative restrictions apply in every country on this technique. In Spain, legislation allows a maximum of three embryos to form each transfer batch. As a result, clinicians prefer to select the embryos by non-invasive embryo examination based on simple methods and observation focused on morphology and dynamics of embryo development after fertilization. This paper proposes the application of Bayesian classifiers to this embryo selection problem in order to provide a decision support system that allows a more accurate selection than with the actual procedures which fully rely on the expertise and experience of embryologists. For this, we propose to take into consideration a reduced subset of feature variables related to embryo morphology and clinical data of patients, and from this data to induce Bayesian classification models. Results obtained applying a filter technique to choose the subset of variables, and the performance of Bayesian classifiers using them, are presented.</description><subject>Artificial Intelligence</subject><subject>Bayes Theorem</subject><subject>Bayesian classifiers</subject><subject>Decision support systems</subject><subject>Decision Support Techniques</subject><subject>Embryo Transfer - methods</subject><subject>Embryo Transfer - statistics & numerical data</subject><subject>Feature subset selection</subject><subject>Female</subject><subject>Fertilization in Vitro - methods</subject><subject>Fertilization in Vitro - statistics & numerical data</subject><subject>Humans</subject><subject>Internal Medicine</subject><subject>IVF outcome prediction</subject><subject>Male</subject><subject>Other</subject><subject>Pregnancy</subject><subject>Zygote - classification</subject><subject>Zygote - ultrastructure</subject><issn>0169-2607</issn><issn>1872-7565</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFksFrFDEYxYModq3-Ax4kJ28zJplJMgERtGhbKPRQPYdM8qWbdWayJjOF_e_NdFcED5YcQsLvPfje-xB6S0lNCRUfdrUd933NCJE1pTWh3TO0oZ1kleSCP0ebAqmKCSLP0Kucd4QQxrl4ic5oRxVRSmxQ-GIOkIOZsB1MzsEHa-YQJ-xjwvMWcIYB7ONP9DhM-CHMKeLtMhYJjH06xIyXHKZ7PMa038Yh3heLAZvJFcswPT6cmc1r9MKbIcOb032Ofnz7-v3iqrq5vby--HxT2VbwrmpBCWqhbfuWcCqtl8o3ThLlHAerpHPeNMwyajvZ9YJxJp1XkoletEz10Jyj90fffYq_FsizHkO2MAxmgrhkLUnblOH5kyBVTcNbKQvIjqBNMecEXu9TGE06aEr02oTe6bUJvTahKdWliSJ6d3Jf-hHcX8kp-gJ8PAJQwngIkHS2ASYLLqSSuHYx_N__0z_yP2n_hNLoLi5pKjFrqjPTRN-tu7CuQjmEKS6b3yA3r_g</recordid><startdate>200805</startdate><enddate>200805</enddate><creator>Morales, Dinora Araceli</creator><creator>Bengoetxea, Endika</creator><creator>Larrañaga, Pedro</creator><creator>García, Miguel</creator><creator>Franco, Yosu</creator><creator>Fresnada, Mónica</creator><creator>Merino, Marisa</creator><general>Elsevier Ireland Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>200805</creationdate><title>Bayesian classification for the selection of in vitro human embryos using morphological and clinical data</title><author>Morales, Dinora Araceli ; Bengoetxea, Endika ; Larrañaga, Pedro ; García, Miguel ; Franco, Yosu ; Fresnada, Mónica ; Merino, Marisa</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4658-4e961ce44b40517cf79f3d709dd5ec97ddfa32c21c878b62527df9726b6429be3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Artificial Intelligence</topic><topic>Bayes Theorem</topic><topic>Bayesian classifiers</topic><topic>Decision support systems</topic><topic>Decision Support Techniques</topic><topic>Embryo Transfer - methods</topic><topic>Embryo Transfer - statistics & numerical data</topic><topic>Feature subset selection</topic><topic>Female</topic><topic>Fertilization in Vitro - methods</topic><topic>Fertilization in Vitro - statistics & numerical data</topic><topic>Humans</topic><topic>Internal Medicine</topic><topic>IVF outcome prediction</topic><topic>Male</topic><topic>Other</topic><topic>Pregnancy</topic><topic>Zygote - classification</topic><topic>Zygote - ultrastructure</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Morales, Dinora Araceli</creatorcontrib><creatorcontrib>Bengoetxea, Endika</creatorcontrib><creatorcontrib>Larrañaga, Pedro</creatorcontrib><creatorcontrib>García, Miguel</creatorcontrib><creatorcontrib>Franco, Yosu</creatorcontrib><creatorcontrib>Fresnada, Mónica</creatorcontrib><creatorcontrib>Merino, Marisa</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Computer methods and programs in biomedicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Morales, Dinora Araceli</au><au>Bengoetxea, Endika</au><au>Larrañaga, Pedro</au><au>García, Miguel</au><au>Franco, Yosu</au><au>Fresnada, Mónica</au><au>Merino, Marisa</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bayesian classification for the selection of in vitro human embryos using morphological and clinical data</atitle><jtitle>Computer methods and programs in biomedicine</jtitle><addtitle>Comput Methods Programs Biomed</addtitle><date>2008-05</date><risdate>2008</risdate><volume>90</volume><issue>2</issue><spage>104</spage><epage>116</epage><pages>104-116</pages><issn>0169-2607</issn><eissn>1872-7565</eissn><abstract>Abstract In vitro fertilization (IVF) is a medically assisted reproduction technique that enables infertile couples to achieve successful pregnancy. Given the uncertainty of the treatment, we propose an intelligent decision support system based on supervised classification by Bayesian classifiers to aid to the selection of the most promising embryos that will form the batch to be transferred to the woman’s uterus. The aim of the supervised classification system is to improve overall success rate of each IVF treatment in which a batch of embryos is transferred each time, where the success is achieved when implantation (i.e. pregnancy) is obtained. Due to ethical reasons, different legislative restrictions apply in every country on this technique. In Spain, legislation allows a maximum of three embryos to form each transfer batch. As a result, clinicians prefer to select the embryos by non-invasive embryo examination based on simple methods and observation focused on morphology and dynamics of embryo development after fertilization. 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subjects | Artificial Intelligence Bayes Theorem Bayesian classifiers Decision support systems Decision Support Techniques Embryo Transfer - methods Embryo Transfer - statistics & numerical data Feature subset selection Female Fertilization in Vitro - methods Fertilization in Vitro - statistics & numerical data Humans Internal Medicine IVF outcome prediction Male Other Pregnancy Zygote - classification Zygote - ultrastructure |
title | Bayesian classification for the selection of in vitro human embryos using morphological and clinical data |
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