<?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>0484-7903</journal-id>
<journal-title><![CDATA[Revista mexicana de anestesiología]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. anestesiol.]]></abbrev-journal-title>
<issn>0484-7903</issn>
<publisher>
<publisher-name><![CDATA[Colegio Mexicano de Anestesiología A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0484-79032025000200094</article-id>
<article-id pub-id-type="doi">10.35366/119202</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Avances en la detección temprana del síndrome de apnea obstructiva del sueño: aplicación integrativa de tecnologías de inteligencia artificial]]></article-title>
<article-title xml:lang="en"><![CDATA[Enhancing early detection of obstructive sleep apnea syndrome: integrative application of artificial intelligence technologies]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramos-Zaga]]></surname>
<given-names><![CDATA[Fernando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Privada del Norte  ]]></institution>
<addr-line><![CDATA[Lima ]]></addr-line>
<country>Peru</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<volume>48</volume>
<numero>2</numero>
<fpage>94</fpage>
<lpage>97</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0484-79032025000200094&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0484-79032025000200094&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0484-79032025000200094&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen:  Introducción:  el síndrome de apnea obstructiva del sueño (SAOS) plantea graves riesgos para la salud, motivo por el cual su detección precoz es crucial para un tratamiento eficaz.  Objetivo:  este trabajo pretende analizar el potencial de la inteligencia artificial (IA) en la detección del SAOS, utilizando específicamente los datos de polisomnografía.  Material y métodos:  para tal fin, se llevó a cabo una revisión bibliográfica mediante una búsqueda exhaustiva de la literatura científica relacionada con el SAOS y su diagnóstico.  Resultados:  de acuerdo a los estudios analizados, los modelos de IA predicen con precisión el riesgo de SAOS. Los métodos de aprendizaje automático resultan prometedores en la revisión de sonidos de ronquidos e imágenes faciales para el diagnóstico del SAOS.  Conclusión:  la tecnología basada en IA mejora el proceso de detección del SAOS mediante métodos no invasivos y eficientes. La incorporación de la IA a múltiples enfoques diagnósticos proporciona una estrategia integral para el diagnóstico precoz del SAOS. Sin embargo, aún es necesaria una mayor validación en diversas poblaciones.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract:  Introduction:  obstructive sleep apnea syndrome (OSAS) poses serious health risks, which is why its early detection is crucial for effective treatment.  Objective:  this paper aims to analyze the potential of artificial intelligence (AI) in the detection of OSAS, specifically using polysomnography data.  Material and methods:  to this end, a literature review was carried out through an exhaustive search of the scientific literature related to OSAS and its diagnosis.  Results:  according to the studies reviewed, AI models accurately predict the risk of OSAS. Machine learning methods show promise in analyzing snoring sounds and facial images for diagnosing OSAS.  Conclusion:  the incorporation of AI into multiple diagnostic approaches provides a comprehensive strategy for the early detection of OSAS. However, further validation in diverse populations is still needed.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[SAOS]]></kwd>
<kwd lng="es"><![CDATA[factores de riesgo]]></kwd>
<kwd lng="es"><![CDATA[diagnóstico]]></kwd>
<kwd lng="es"><![CDATA[tratamiento]]></kwd>
<kwd lng="es"><![CDATA[prevalencia]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="en"><![CDATA[OSAS]]></kwd>
<kwd lng="en"><![CDATA[risk factors]]></kwd>
<kwd lng="en"><![CDATA[diagnosis]]></kwd>
<kwd lng="en"><![CDATA[treatment]]></kwd>
<kwd lng="en"><![CDATA[prevalence]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
</kwd-group>
</article-meta>
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