<?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>S1405-99402023001000094</article-id>
<article-id pub-id-type="doi">10.24875/acm.220001481</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Precisión diagnóstica del software de cuantificación automática en pacientes con sospecha de COVID-19 del Instituto Nacional de Cardiología Ignacio Chávez]]></article-title>
<article-title xml:lang="en"><![CDATA[Diagnostic accuracy of the automatic quantification software in patients with suspected COVID-19 of the Instituto Nacional de Cardiology Ignacio Chávez]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gallego-Díaz]]></surname>
<given-names><![CDATA[Estefanía]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cristancho-Rojas]]></surname>
<given-names><![CDATA[César N.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Criales-Vera]]></surname>
<given-names><![CDATA[Sergio A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Nacional de Cardiología Ignacio Chávez Departamento de Radiología Programa de Imagenología Diagnóstica y Terapéutica]]></institution>
<addr-line><![CDATA[ Ciudad de México]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional Autónoma de México Grupo CT Scanner ]]></institution>
<addr-line><![CDATA[ Ciudad de México]]></addr-line>
<country>México</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Salud Digna Departamento de Radiología ]]></institution>
<addr-line><![CDATA[ Ciudad de México]]></addr-line>
<country>México</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Instituto Nacional de Cardiología Ignacio Chávez Departamento de Radiología e Imagen ]]></institution>
<addr-line><![CDATA[ Ciudad de México]]></addr-line>
<country>México</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Grupo CT Scanner Servicio de Tomografía y PET ]]></institution>
<addr-line><![CDATA[ Ciudad de México]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<volume>93</volume>
<fpage>94</fpage>
<lpage>101</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-99402023001000094&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-99402023001000094&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-99402023001000094&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Antecedentes y Objetivos: Establecer la precisión diagnóstica por tomografía computarizada (TC) de la probabilidad de neumopatía por enfermedad por coronavirus 2019 (COVID-19), dada por el sistema de inteligencia artificial (IA) diseñado por Siemens, y el resultado de la evaluación cualitativa CO-RADS (COVID-19 Reporting and Data System) con el estándar de referencia reacción en cadena de la polimerasa transcriptasa inversa (RT-PCR), entregando así la experiencia de nuestra institución.  Métodos: Se realizó un estudio observacional, comparativo y retrolectivo en 192 pacientes adultos con sospecha de infección por coronavirus 2 del síndrome respiratorio agudo grave (SARS-CoV-2) que contaban con prueba PCR. Se obtuvo la información de precisión diagnóstica luego de comparar el estándar de referencia (RT- PCR) con el CO-RADS realizado por los observadores y la probabilidad de COVID-19 que arrojaron las imágenes de TC mediante la IA.  Resultados: La comparación de la probabilidad de COVID-19 obtenida por la IA vs. la RT-PCR para SARS-CoV- 2 generó un AUC ROC de 0.774 (IC: 0.69-0.81) con p = 0.0001. La probabilidad de COVID-19 tuvo una precisión aceptable, con un buen valor predictivo positivo del 87.80%, pero con un pobre valor predictivo negativo del 58.80%. La variable CO-RADS vs. PCR obtuvo una mayor precisión con valores de sensibilidad y especificidad del 91.80 y 88.7% respectivamente.  Conclusión: La comparación entre los resultados obtenidos por la IA y por la variable CO-RADS mostró mayor efectividad en esta última, sin embargo se logró documentar el alto impacto que tiene el sistema de cuantificación automática en la evaluación de estos pacientes, ya que permite agilizar la valoración del radiólogo y funciona como complemento en casos de dudas diagnósticas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Background and Objectives: To establish the diagnostic accuracy of the computed tomography (CT) comparing the probability of COVID-19 pneumopathy, obtained through artificial intelligence (AI) system designed by Siemens Healthineers, and the qualitative evaluation CO-RADS (COVID-19 Reporting and Data System) with the reference standard (RT-PCR), and thus providing the experience of our institution.  Methods: An observational, comparative and retrolective study was performed on 280 adult patients with suspected SARS-CoV2 infection, 192 of whom had PCR testing. Diagnostic accuracy information was obtained after comparing the reference standard (RT-PCR) with the CO-RADS performed by observers and the probability of COVID-19 yielded by CT images through AI software.  Results: The comparison of COVID-19 probability acquired by AI vs. SARS CoV-2 RT-PCR generated an AUC ROC 0.774 (CI 0.69-0.81) with p = 0.0001. The COVID-19 probability had an acceptable accuracy, with a good PPV 87.80%, but with a poor NPV of 58.80%. The CO-RADS vs. RCP variable got a higher accuracy with much higher sensitivity and specificity values, reaching 91.80% and 88.7% respectively.  Conclusion: The comparison between the results obtained by the AI and those referring to the CO-RADS variable showed greater effectiveness in the latter for patients with suspected COVID-19 however, it was possible to document the high impact of the automatic quantification system in the evaluation of these patients since it allows speeding up the radiologist&#8217;s assessment and works as a complement in cases of diagnostic doubts.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[Cuantificación automática]]></kwd>
<kwd lng="es"><![CDATA[COVID-19]]></kwd>
<kwd lng="es"><![CDATA[CO-RADS]]></kwd>
<kwd lng="es"><![CDATA[RT-PCR]]></kwd>
<kwd lng="en"><![CDATA[Artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[Automatic quantification]]></kwd>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[CO-RADS]]></kwd>
<kwd lng="en"><![CDATA[RT-PCR]]></kwd>
</kwd-group>
</article-meta>
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