<?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>1405-9940</journal-id>
<journal-title><![CDATA[Archivos de cardiología de México]]></journal-title>
<abbrev-journal-title><![CDATA[Arch. Cardiol. Méx.]]></abbrev-journal-title>
<issn>1405-9940</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Cardiología Ignacio Chávez]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-99402025000200178</article-id>
<article-id pub-id-type="doi">10.24875/acm.24000195</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Derivation of an artificial intelligence-based electrocardiographic model for the detection of acute coronary occlusive myocardial infarction]]></article-title>
<article-title xml:lang="es"><![CDATA[Derivación de un modelo electrocardiográfico basado en inteligencia artificial para la detección de infarto agudo del miocardio por oclusión trombótica]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Díaz-Herrera]]></surname>
<given-names><![CDATA[Braiana A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Roman-Rangel]]></surname>
<given-names><![CDATA[Edgar]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castro-García]]></surname>
<given-names><![CDATA[Carlos A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sierra-Lara Martinez]]></surname>
<given-names><![CDATA[Daniel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gopar-Nieto]]></surname>
<given-names><![CDATA[Rodrigo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Velez-Talavera]]></surname>
<given-names><![CDATA[Karen G.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Espinosa-Martínez]]></surname>
<given-names><![CDATA[María P.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[March-Mifsut]]></surname>
<given-names><![CDATA[Santiago]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Latapi-Ruiz-Esparza]]></surname>
<given-names><![CDATA[Ximena]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Preciado-Gutierrez]]></surname>
<given-names><![CDATA[Oscar U.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Alba-Valencia]]></surname>
<given-names><![CDATA[Santiago]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sánchez-Alfaro]]></surname>
<given-names><![CDATA[Héctor A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gonzalez-Pacheco]]></surname>
<given-names><![CDATA[Héctor]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Arias-Mendoza]]></surname>
<given-names><![CDATA[Alexandra]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Araiza-Garaygordobil]]></surname>
<given-names><![CDATA[Diego]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Nacional de Cardiología Ignacio Chávez Unidad Coronaria ]]></institution>
<addr-line><![CDATA[ Ciudad de México]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Tecnológico Autónomo de México Departamento Académico de Computación ]]></institution>
<addr-line><![CDATA[ Ciudad de México]]></addr-line>
<country>México</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Fundación Mexicana para la Salud Coordinación de Nuevas Tecnologías ]]></institution>
<addr-line><![CDATA[ Ciudad de México]]></addr-line>
<country>México</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>95</volume>
<numero>2</numero>
<fpage>178</fpage>
<lpage>187</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-99402025000200178&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-99402025000200178&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-99402025000200178&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Objectives: We aimed to assess the performance of an artificial intelligence&#8211;electrocardiogram (AI-ECG)-based model capable of detecting acute coronary occlusion myocardial infarction (ACOMI) in the setting of patients with acute coronary syndrome (ACS).  Methods: This was a prospective, observational, longitudinal, and single-center study including patients with the initial diagnosis of ACS (both ST-elevation acute myocardial infarction [STEMI] &amp; non-ST-segment elevation myocardial infarction [NSTEMI]). To train the deep learning model in recognizing ACOMI, manual digitization of a patient's ECG was conducted using smartphone cameras of varying quality. We relied on the use of convolutional neural networks as the AI models for the classification of ECG examples. ECGs were also independently evaluated by two expert cardiologists blinded to clinical outcomes; each was asked to determine (a) whether the patient had a STEMI, based on universal criteria or (b) if STEMI criteria were not met, to identify any other ECG finding suggestive of ACOMI. ACOMI was defined by coronary angiography findings meeting any of the following three criteria: (a) total thrombotic occlusion, (b) TIMI thrombus grade 2 or higher + TIMI grade flow 1 or less, or (c) the presence of a subocclusive lesion (&gt; 95% angiographic stenosis) with TIMI grade flow &lt; 3. Patients were classified into four groups: STEMI + ACOMI, NSTEMI + ACOMI, STEMI + non-ACOMI, and NSTEMI + non-ACOMI.  Results: For the primary objective of the study, AI outperformed human experts in both NSTEMI and STEMI, with an area under the curve (AUC) of 0.86 (95% confidence interval [CI] 0.75-0.98) for identifying ACOMI, compared with ECG experts using their experience (AUC: 0.33, 95% CI 0.17-0.49) or under universal STEMI criteria (AUC: 0.50, 95% CI 0.35-0.54), (p value for AUC receiver operating characteristic comparison &lt; 0.001). The AI model demonstrated a PPV of 0.84 and an NPV of 1.0.  Conclusion: Our AI-ECG model demonstrated a higher diagnostic precision for the detection of ACOMI compared with experts and the use of STEMI criteria. Further research and external validation are needed to understand the role of AI-based models in the setting of ACS.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Objetivos: Nuestro objetivo fue evaluar el rendimiento de un modelo electrocardiográfico basado en IA capaz de detectar ACOMI (Acute Coronary Occlusion Myocardial Infarction) en pacientes con SCA.  Métodos: Este fue un estudio prospectivo, observacional y longitudinal, de un solo centro que incluyó a pacientes con diagnóstico inicial de SCA (tanto STEMI como NSTEMI). Para entrenar el modelo de deep learning en el reconocimiento de ACOMI, se realizó una digitalización manual de los ECG de los pacientes utilizando cámaras de teléfonos inteligentes de diversas calidades. Nos basamos en el uso de Redes Neuronales Convolucionales (CNN) como modelos de inteligencia artificial para la clasificación de los ejemplos de ECG. Los ECG fueron evaluados de forma independiente por dos cardiólogos expertos, quienes desconocían los resultados clínicos; a cada uno se le pidió determinar a) si el paciente presentaba un STEMI, según criterios universales, o b) si no se cumplían los criterios de STEMI, identificar cualquier otro hallazgo en el ECG que sugiriera ACOMI. ACOMI se definió por la presencia de cualquiera de los siguientes tres hallazgos en la angiografía coronaria: a) oclusión total trombótica, b) trombo grado TIMI 2 o superior + flujo grado TIMI 1 o menor, o c) la presencia de una lesión suboclusión (&gt; 95% de estenosis angiográfica) con flujo grado TIMI &lt; 3. Los pacientes se clasificaron en cuatro grupos: STEMI + ACOMI, NSTEMI + ACOMI, STEMI + no ACOMI y NSTEMI + no ACOMI.  Resultados: Para el objetivo principal del estudio, la IA superó a los expertos humanos tanto en NSTEMI como en STEMI, con un AUC de 0.86 (IC del 95%: 0.75-0.98) para identificar ACOMI, en comparación con los expertos en ECG utilizando su experiencia (AUC: 0.33, IC del 95%: 0.17-0.49) o bajo los criterios universales de STEMI (AUC: 0.50, IC del 95%: 0.35-0.54), (valor p para la comparación del AUC ROC &lt; 0.001). El modelo de IA demostró un VPP de 0.84 y un VPN de 1.0.  Conclusiones: Nuestro modelo de ECG basado en IA demostró una mayor precisión diagnóstica para la detección de ACOMI en comparación con los expertos y el uso de los criterios de STEMI. Se necesita más investigación y validación externa para comprender el papel de los modelos basados en IA en el contexto del SCA.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Occlusion myocardial infarction]]></kwd>
<kwd lng="en"><![CDATA[NSTEMI]]></kwd>
<kwd lng="en"><![CDATA[Artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[Acute coronary syndrome]]></kwd>
<kwd lng="en"><![CDATA[Deep learning]]></kwd>
<kwd lng="en"><![CDATA[Transfer learning]]></kwd>
<kwd lng="es"><![CDATA[Infarto agudo de miocardio por oclusión]]></kwd>
<kwd lng="es"><![CDATA[NSTEMI]]></kwd>
<kwd lng="es"><![CDATA[Inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[Síndrome coronario agudo]]></kwd>
<kwd lng="es"><![CDATA[Deep learning]]></kwd>
<kwd lng="es"><![CDATA[Transfer learning]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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