<?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>0376-7892</journal-id>
<journal-title><![CDATA[Cirugía Plástica Ibero-Latinoamericana]]></journal-title>
<abbrev-journal-title><![CDATA[Cir. plást. iberolatinoam.]]></abbrev-journal-title>
<issn>0376-7892</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Española de Cirugía Plástica, Reparadora y Estética (SECPRE)]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0376-78922024000400012</article-id>
<article-id pub-id-type="doi">10.4321/s0376-78922024000400012</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelo de aprendizaje profundo para el desarrollo de parámetros visuales de valoración del labio superior en pacientes operados de fisura labial unilateral]]></article-title>
<article-title xml:lang="en"><![CDATA[Deep learning model for the development of visual parameters for upper lip assessing in patients operated on for unilateral cleft lip]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rossell-Perry]]></surname>
<given-names><![CDATA[Percy]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
<xref ref-type="aff" rid="Ab"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Arias-Figueroa]]></surname>
<given-names><![CDATA[Jhosimar]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Peruana Union Facultad de Medicina Humana ]]></institution>
<addr-line><![CDATA[Lima ]]></addr-line>
<country>Perú</country>
</aff>
<aff id="A1b">
<institution><![CDATA[,Universidad Católica de Murcia  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>España</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Peruana de Ciencias Aplicadas  ]]></institution>
<addr-line><![CDATA[Lima ]]></addr-line>
<country>Perú</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>50</volume>
<numero>4</numero>
<fpage>459</fpage>
<lpage>468</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.isciii.es/scielo.php?script=sci_arttext&amp;pid=S0376-78922024000400012&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.isciii.es/scielo.php?script=sci_abstract&amp;pid=S0376-78922024000400012&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.isciii.es/scielo.php?script=sci_pdf&amp;pid=S0376-78922024000400012&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Introducción y objetivo. La fisura labio palatina es un defecto congénito (la segunda malformación congénita más frecuente) de la cara, que afecta a la formación del labio superior, región alveolar y palatina. Su impacto social es grande; los pacientes se ven afectados psicológica y funcionalmente, pudiendo presentar defectos del habla y la alimentación así como secuelas estéticas que generan ansiedad, depresión, baja autoestima, etc. El objetivo principal del presente estudio es desarrollar un modelo de aprendizaje profundo (deep learning) que permita valorar parámetros visuales labiales superiores postoperatorios en pacientes operados de fisura labial unilateral.  Material y método. Coleccionamos imágenes postoperatorias de 364 pacientes operados de fisura labial unilateral, calificadas por 3 revisores expertos como resultados buenos o malos. Entrenamos 3 modelos de redes convolucionales neuronales para determinar la simetría labial y la calidad del resultado postoperatorio y los comparamos en base a su sensibilidad, especificidad y curva ROC-AUC.  Resultados. Los 3 modelos de redes neuronales convolucionales usados fueron eficaces en la detección de la calidad de la cirugía realizada en pacientes con fisuras labiales unilaterales, siendo los modelos MobileNet y EfficientNet los que tuvieron mejores resultados con valores por encima del 80 % en todas las métricas.  Conclusiones. El modelo de inteligencia artificial utilizado ha demostrado su habilidad para medir la simetría labial postoperatoria en pacientes operados de fisura labial unilateral. Los mejores modelos fueron MobileNet y EfficientNet con métricas por encima del 80 %. Esta eficiencia se puede mejorar aumentando el número de datos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Background and objective. Cleft lip and palate is a congenital defect (the second most frecuente congenital malformation) of the face that affects the formation of the upper lip, alveolar and palatine region. The social impact that this pathology represents is great; patients are affected psychologically and functionally, and may present speech and eating defectsas well as aesthetic consequences that generate anxiety, depression, low self-esteem and others. The main objective of our study is to develop a deep learning model that allows assessing postoperative upper labial visual parameters in patients operated on for unilateral cleft lip.  Methods. Postoperative images of 364 patients operated on for unilateral cleft lip were collected and rated by 3 expert reviewers as good or poor results. Three convolutional neural network models were trained to determine lip symmetry and postoperative outcome quality and were compared based on their sensitivity, specificity, and ROC-AUC curve.  Results. The 3 convolutional neural network models used were effective in detecting the quality of surgery performed in patients with unilateral cleft lips, with the MobileNet and EfficientNet models having the best results with values above 80% in all metrics.  Conclusions. The artificial intelligence models used have demonstrated their ability to measure postoperative lip symmetry in patients operated on for unilateral cleft lip. The best models were MobileNet and EfficientNet with metrics above 80%. This efficiency can be improved by increasing the number of data.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[Deep learning]]></kwd>
<kwd lng="en"><![CDATA[Cleft lip and palate classification]]></kwd>
<kwd lng="es"><![CDATA[Inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[Aprendizaje profundo]]></kwd>
<kwd lng="es"><![CDATA[Clasificación fisuras labio palatinas]]></kwd>
</kwd-group>
</article-meta>
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