<?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>S2341-45452024000600032</article-id>
<article-id pub-id-type="doi">10.1159/000539837</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Deep Learning and Minimally Invasive Endoscopy: Panendoscopic Detection of Pleomorphic Lesions]]></article-title>
<article-title xml:lang="pt"><![CDATA[Deep Learning e Endoscopia Minimamente Invasiva: Deteção panendoscópica de lesões pleomór&#64257;cas]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mascarenhas]]></surname>
<given-names><![CDATA[Miguel]]></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[Mendes]]></surname>
<given-names><![CDATA[Francisco]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</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"/>
<xref ref-type="aff" rid="A a"/>
<xref ref-type="aff" rid="A3"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Afonsoa]]></surname>
<given-names><![CDATA[João]]></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[Cardoso]]></surname>
<given-names><![CDATA[Pedro Marílio]]></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[Martins]]></surname>
<given-names><![CDATA[Miguel]]></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[Ferreira]]></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[Saraiva]]></surname>
<given-names><![CDATA[Miguel Mascarenhas]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</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 Precision Medicine Unit]]></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[,Digestive Arti&#64257;cial Intelligence Development  ]]></institution>
<addr-line><![CDATA[Porto ]]></addr-line>
<country>Portugal</country>
</aff>
<aff id="Af6">
<institution><![CDATA[,ManopH Gastroenterology Clinic  ]]></institution>
<addr-line><![CDATA[Porto ]]></addr-line>
<country>Portugal</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<volume>31</volume>
<numero>6</numero>
<fpage>32</fpage>
<lpage>42</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_arttext&amp;pid=S2341-45452024000600032&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_abstract&amp;pid=S2341-45452024000600032&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_pdf&amp;pid=S2341-45452024000600032&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Introduction:  Capsule endoscopy (CE) is a minimally invasive exam suitable of panendoscopic evaluation of the gastrointestinal (GI) tract. Nevertheless, CE is time-consuming with suboptimal diagnostic yield in the upper GI tract. Convolutional neural networks (CNN) are human brain architecture-based models suitable for image analysis. However, there is no study about their role in capsule panendoscopy.  Methods:  Our group developed an arti&#64257;cial intelligence (AI) model for panendoscopic automatic detection of pleomorphic lesions (namely vascular lesions, protuberant lesions, hematic residues, ulcers, and erosions). 355,110 images (6,977 esophageal, 12,918 gastric, 258,443 small bowel, 76,772 colonic) from eight different CE and colon CE (CCE) devices were divided into a training and validation dataset in a patient split design. The model classi&#64257;cation was compared to three CE experts&#8217; classi&#64257;cation. The model&#8217;sperformance wasevaluated by itssensitivity, speci&#64257;city, accuracy, positive predictive value, negative predictive value, and area under the precision-recall curve.  Results:  The binary esophagus CNN had a diagnostic accuracy for pleomorphic lesions of 83.6%. The binary gastric CNN identi&#64257;ed pleomorphic lesions with a 96.6% accuracy. The undenary small bowel CNN distinguished pleomorphic lesions with different hemorrhagic potentials with 97.6% accuracy. The trinary colonic CNN (detection and differentiation of normal mucosa, pleomorphic lesions, and hematic residues) had 94.9% global accuracy.  Discussion/Conclusion:  We developed the &#64257;rst AI model for panendoscopic automatic detection of pleomorphic lesions in both CE and CCE from multiple brands, solving a critical interoperability technological challenge. Deep learning-based tools may change the landscape of minimally invasive capsule panendoscopy.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo  Introdução:  A endoscopia por cápsula (EC) é um exame minimamente invasivo que avalia todo o trato gastrointestinal. Contudo, é morosa, com acuidade limitada no trato digestivo superior. As redes convolucionais neurais (RCN) são modelos baseados na arquitetura cerebral humana aperfeiçoados para análise de imagens. Contudo, o seu papel na panendoscopia por cápsula ainda não foi estudado.  Métodos:  Desenvolveu-se um modelo de inteligência arti&#64257;cial (IA) para deteção panendoscópica de lesões pleomór&#64257;cas (nomeadamente lesões vasculares, protuberantes, resíduos hemáticos, úlceras e erosões). 355,110 imagens (6,977 esofágicas, 12,918 gástricas, 258,443 do intestino delgado e 76,772 colónicas) de oito dispositivos diferentes de enteroscopia e panendoscopia por cápsula foram divididas num dataset de treino e validação num desenho patient split. Aclassi&#64257;cação da RCN comparou-se com a de três especialistas em CE. O modelo foi avaliado através da sensibilidade, especi&#64257;cidade, valor preditivo positivo, valor preditivo negativo, acuidade e área sob curva precision-recall.  Resultados:  A RCN binária esofágica teve acuidade de 83.6%para lesões pleomór&#64257;cas. A RCN binária para lesões gástricas pleomór&#64257;cas teve acuidade de 96.6%. ARCN de 11 categorias de intestino delgado diferenciou lesões pleomór&#64257;cas com diferente potencial hemorrágico com acuidade de 97.6%. A RCN trinaria colónica (mucosa normal, lesões pleomór&#64257;cas e resíduos hemáticos) teve acuidade de 94.9%.  Discussão/Conclusão:  Desenvolveu-se o primeiro modelo de IA com elevada acuidade na deteção panendoscópica de lesões pleomór&#64257;cas em dispositivos de enteroscopia e panendoscopia por cápsula, solucionando um desa&#64257;ode interoperabilidade tecnológica. A utilização de modelos de deep learning pode alterar o panorama da panendoscopia por cápsula.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Arti&#64257;cial intelligence]]></kwd>
<kwd lng="en"><![CDATA[Capsule endoscopy]]></kwd>
<kwd lng="en"><![CDATA[Deep learning]]></kwd>
<kwd lng="en"><![CDATA[Panendoscopy]]></kwd>
<kwd lng="pt"><![CDATA[Deep learning]]></kwd>
<kwd lng="pt"><![CDATA[Endoscopia por cápsula]]></kwd>
<kwd lng="pt"><![CDATA[Inteligência arti&#64257;cial]]></kwd>
<kwd lng="pt"><![CDATA[Panendoscopia]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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