<?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>2341-4545</journal-id>
<journal-title><![CDATA[GE-Portuguese Journal of Gastroenterology]]></journal-title>
<abbrev-journal-title><![CDATA[GE Port J Gastroenterol]]></abbrev-journal-title>
<issn>2341-4545</issn>
<publisher>
<publisher-name><![CDATA[Sociedade Portuguesa de Gastrenterologia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2341-45452022000500033</article-id>
<article-id pub-id-type="doi">10.1159/000518901</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Small Bowel Blood Content Using a Convolutional Neural Network]]></article-title>
<article-title xml:lang="pt"><![CDATA[Inteligência artificial e endoscopia por cápsula: Deteção automática de conteúdo hemático entérico através de uma rede neural convolucional]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Saraiva]]></surname>
<given-names><![CDATA[Miguel Mascarenhas]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
<xref ref-type="aff" rid="A a"/>
<xref ref-type="aff" rid="A3"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ribeiro]]></surname>
<given-names><![CDATA[Tiago]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Afonso]]></surname>
<given-names><![CDATA[João]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ferreira]]></surname>
<given-names><![CDATA[João P.S.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cardoso]]></surname>
<given-names><![CDATA[Hélder]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
<xref ref-type="aff" rid="A a"/>
<xref ref-type="aff" rid="A3"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Andrade]]></surname>
<given-names><![CDATA[Patrícia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
<xref ref-type="aff" rid="A a"/>
<xref ref-type="aff" rid="A3"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Parente]]></surname>
<given-names><![CDATA[Marco P.L.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Jorge]]></surname>
<given-names><![CDATA[Renato N.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Macedo]]></surname>
<given-names><![CDATA[Guilherme]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
<xref ref-type="aff" rid="A a"/>
<xref ref-type="aff" rid="A3"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,São João University Hospital Department of Gastroenterology ]]></institution>
<addr-line><![CDATA[Porto ]]></addr-line>
<country>Portugal</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,WGO Gastroenterology and Hepatology Training Center  ]]></institution>
<addr-line><![CDATA[Porto ]]></addr-line>
<country>Portugal</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,University of Porto Faculty of Medicine ]]></institution>
<addr-line><![CDATA[Porto ]]></addr-line>
<country>Portugal</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,University of Porto Faculty of Engineering Department of Mechanical Engineering]]></institution>
<addr-line><![CDATA[Porto ]]></addr-line>
<country>Portugal</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,INEGI - Institute of Science and Innovation in Mechanical and Industrial Engineering  ]]></institution>
<addr-line><![CDATA[Porto ]]></addr-line>
<country>Portugal</country>
</aff>
<pub-date pub-type="pub">
<day>30</day>
<month>10</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>30</day>
<month>10</month>
<year>2022</year>
</pub-date>
<volume>29</volume>
<numero>5</numero>
<fpage>33</fpage>
<lpage>40</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_arttext&amp;pid=S2341-45452022000500033&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_abstract&amp;pid=S2341-45452022000500033&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_pdf&amp;pid=S2341-45452022000500033&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Introduction:  Capsule endoscopy has revolutionized the management of patients with obscure gastrointestinal bleeding. Nevertheless, reading capsule endoscopy images is time-consuming and prone to overlooking significant lesions, thus limiting its diagnostic yield. We aimed to create a deep learning algorithm for automatic detection of blood and hematic residues in the enteric lumen in capsule endoscopy exams.  Methods:  A convolutional neural network was developed based on a total pool of 22,095 capsule endoscopy images (13,510 images containing luminal blood and 8,585 of normal mucosa or other findings). A training dataset comprising 80% of the total pool of images was defined. The performance of the network was compared to a consensus classification provided by 2 specialists in capsule endoscopy. Subsequently, we evaluated the performance of the network using an independent validation dataset (20% of total image pool), calculating its sensitivity, specificity, accuracy, and precision.  Results:  Our convolutional neural network detected blood and hematic residues in the small bowel lumen with an accuracy and precision of 98.5 and 98.7%, respectively. The sensitivity and specificity were 98.6 and 98.9%, respectively. The analysis of the testing dataset was completed in 24 s (approximately 184 frames/s).  Discussion/Conclusion:  We have developed an artificial intelligence tool capable of effectively detecting luminal blood. The development of these tools may enhance the diagnostic accuracy of capsule endoscopy when evaluating patients presenting with obscure small bowel bleeding.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo  Introdução:  A endoscopia por cápsula revolucionou a abordagem a doentes com hemorragia digestiva obscura. No entanto, a leitura de imagens de endoscopia por cápsula é morosa, havendo suscetibilidade para a perda de lesões significativas, limitando desta forma a sua eficácia diagnóstica. Este estudo visou a criação de um algoritmo de deep learning para deteção automática de sangue e resíduos hemáticos no lúmen entérico usando imagens de endoscopia por cápsula.  Métodos:  Foi desenvolvida uma rede neural convolucional com base num conjunto de 22,095 imagens de endoscopia de cápsula (13,510 imagens contendo sangue e 8,585 mucosa normal ou outros achados). Foi construído um grupo de imagens para treino, compreendendo 80% do total de imagens. O desempenho da rede foi comparado com a classificação consenso de dois especialistas em endoscopia por cápsula. Posteriormente, o desempenho da rede foi avaliado usando os restantes 20% de imagens. Foi calculada a sua sensibilidade, especificidade, exatidão e precisão.  Resultados:  O algoritmo detetou sangue e resíduos hemáticos no lúmen do intestino delgado com uma exatidão e precisão de 98.5% e 98.7%, respetivamente. A sensibilidade e especificidade foram 98.6% e 98.9%, respetivamente. A análise do conjunto de usado para teste da rede foi concluída em 24 segundos (aproximadamente 184 frames/s).  Discussão/Conclusão:  Foi desenvolvida uma ferramenta de inteligência artificial capaz de detetar efetivamente o sangue luminal. O desenvolvimento dessas ferramentas pode aumentar a precisão do diagnóstico da endoscopia por cápsula ao avaliar pacientes que apresentam sangramento obscuro do intestino delgado.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Capsule endoscopy]]></kwd>
<kwd lng="en"><![CDATA[Artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[Convolutional neural networks]]></kwd>
<kwd lng="en"><![CDATA[Small bowel]]></kwd>
<kwd lng="en"><![CDATA[Gastrointestinal bleeding]]></kwd>
<kwd lng="pt"><![CDATA[Endoscopia por cápsula]]></kwd>
<kwd lng="pt"><![CDATA[Inteligência artificial]]></kwd>
<kwd lng="pt"><![CDATA[Intestino delgado]]></kwd>
<kwd lng="pt"><![CDATA[Hemorragia gastrointestinal]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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