<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2007-7467</journal-id>
<journal-title><![CDATA[RIDE. Revista Iberoamericana para la Investigación y el Desarrollo Educativo]]></journal-title>
<abbrev-journal-title><![CDATA[RIDE. Rev. Iberoam. Investig. Desarro. Educ]]></abbrev-journal-title>
<issn>2007-7467</issn>
<publisher>
<publisher-name><![CDATA[Centro de Estudios e Investigaciones para el Desarrollo Docente A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-74672021000100118</article-id>
<article-id pub-id-type="doi">10.23913/ride.v11i22.856</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Clasificación automática de anastomosis mediante redes neuronales convolucionales en video fetoscópico]]></article-title>
<article-title xml:lang="en"><![CDATA[An Automatic Classification of Anastomosis by Convolutional Neural Networks in Fetoscopic Video]]></article-title>
<article-title xml:lang="pt"><![CDATA[Classificação automática de anastomoses usando redes neurais convolucionais em vídeo fetoscópico]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lerma Sánchez]]></surname>
<given-names><![CDATA[Ángel Mario]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mexicano Santoyo]]></surname>
<given-names><![CDATA[Adriana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Villalobos Castaldi]]></surname>
<given-names><![CDATA[Fabiola Miroslaba]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Damián Reyes]]></surname>
<given-names><![CDATA[Pedro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Da Vinci  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Tecnológico Nacional de México Instituto Tecnológico de Cd. Victoria ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Da Vinci  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad de Colima  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<volume>11</volume>
<numero>22</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-74672021000100118&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-74672021000100118&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-74672021000100118&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[resumen está disponible en el texto completo]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Twin-twin transfusion syndrome (TTTS) is the result of uneven blood flow through placental vascular anastomosis (blood vessel connection) that link the two fetal circulations. Vascular anastomosis in the shared placenta are present in virtually all monocorionic twin pregnancies (MCs), but in only about 10% lead to twin-twin transfusion syndrome. Without intervention, the condition is often fatal for both twins. An alternative to TTTS treatment is the placental laser procedure known as fetal surgery, which consists, in a very general way, of splitting the placenta in two, by laser cauterization of blood vessels between fetuses, thus balancing blood flows. Currently fetoscopic surgery is a procedure that is performed frequently in Mexico and the appropriate classification of anastomosis is vital for this surgery, since it represents the most recommended treatment. However, die to its degree of complexity, this surgical intervention presents multiple difficulties, such as fetuses moving during the procedure, the orientation of the video used is not suitable for a more accurate analysis, the field of view generated by the fetus is very small. Therefore, it is necessary to have a tool that helps the doctor to be able to differentiate and classify anastomosis in a more appropriate way. The objective of this work is to present the development of a computational tool that contributes to the automatic classification of anastomosis within a fetoscopic video through convolutional neural networks in a way that serves as a support for the unexperienced doctor in his training stage. A DataSet (image set) was built from fetoscopic videos first unclassified, then cataloged into three categories, selected in conjunction with experts, using an computational tool specifically created for this purpose (VideoLabel). The data augmentation technique served to build artificial images from the actual ones already classified, since the number of tagged images were not sufficient; in the same context, the AlexNet architecture was selected to perform a learning transfer and be trained to obtain results above 90% effectiveness in the classifications made by the computational tool created. This data allows us to conclude that if we did not have a tool that would allow first-time physicians to train in the identification of anastomosis as a practice prior to fetal surgery, their training will take longer since it is done in situ in each fetal surgery. As a result of this research, the design of a software was generated with which it is feasible to automatically classify anastomosis from a fetoscopic video through a convolutional neural network with promising results as a tool to support doctors in their training stage, allowing them to carry out their training in a more agile and less timely way.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo A síndrome da transfusão de gêmeos (TTTS) é o resultado do fluxo sanguíneo desigual através das anastomoses vasculares da placenta (conexão dos vasos sanguíneos) ligando as duas circulações fetais. Anastomoses vasculares na placenta compartilhada estão presentes em praticamente todos os gêmeos monocoriônicos (MCs), mas apenas em cerca de 10% levam à síndrome de transfusão de gêmeos. Sem intervenção, a condição costuma ser fatal para ambos os gêmeos. Uma alternativa para o tratamento do TTTS é o procedimento a laser placentário, conhecido como cirurgia fetal, que geralmente consiste em dividir a placenta em duas por meio da cauterização a laser dos vasos sanguíneos entre os fetos, equilibrando assim os fluxos sanguíneos. Atualmente, a cirurgia fetoscópica é um procedimento muito realizado no país, portanto a classificação da anastomose é fundamental para esta cirurgia, que é o tratamento mais recomendado. Devido ao seu grau de complexidade, esta intervenção cirúrgica apresenta múltiplas dificuldades: os fetos movimentam-se durante o procedimento, a orientação do vídeo utilizado não é adequada para uma análise mais precisa e o campo de visão gerado pelo fetoscópio é muito pequeno; portanto, é necessário um instrumento que ajude o médico novato a diferenciar e classificar as anastomoses de forma mais adequada. O objetivo deste trabalho, portanto, é apresentar o desenvolvimento de uma ferramenta computacional que contribua para a classificação automática de anastomose em um vídeo fetoscópico por meio de redes neurais convolucionais de forma a servir de suporte para o médico iniciante em seu estágio de treinamento. Para isso, um DataSet (conjunto de imagens) foi construído a partir de vídeos fetoscópicos não classificados que foram então catalogados em três categorias, selecionadas em conjunto com os especialistas, usando uma ferramenta computacional especialmente projetada (VideoLabel). A técnica de aumento de dados serviu para construir imagens artificiais a partir das reais já classificadas, uma vez que o número de imagens rotuladas não era suficiente; Neste contexto, a arquitetura AlexNet foi selecionada para realizar uma transferência de aprendizagem e para treinamento, que rendeu resultados com uma eficácia superior a 90%. Esses dados permitem concluir que, na ausência de uma ferramenta que possibilitasse o treinamento de novos médicos na identificação de anastomoses, o seu treinamento demoraria mais, pois seria realizado in loco em cada cirurgia fetal. No entanto, esta pesquisa gerou o desenho de um software com o qual é viável a classificação automática de anastomoses de um vídeo fetoscópico utilizando uma rede neural convolucional, com resultados promissores como ferramenta de apoio a médicos em estágio de formação.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[aprendizaje de máquina]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje supervisado]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[síndrome de transfusión gemelo a gemelo]]></kwd>
<kwd lng="en"><![CDATA[Machine Learning]]></kwd>
<kwd lng="en"><![CDATA[Supervised Learning]]></kwd>
<kwd lng="en"><![CDATA[Artificial Intelligence]]></kwd>
<kwd lng="en"><![CDATA[Twin to Twin Transfusion Syndrome]]></kwd>
<kwd lng="pt"><![CDATA[aprendizado de máquina]]></kwd>
<kwd lng="pt"><![CDATA[aprendizado supervisionado]]></kwd>
<kwd lng="pt"><![CDATA[inteligência artificial]]></kwd>
<kwd lng="pt"><![CDATA[síndrome de transfusão de gêmeos]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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