<?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>2306-4102</journal-id>
<journal-title><![CDATA[Acta ortopédica mexicana]]></journal-title>
<abbrev-journal-title><![CDATA[Acta ortop. mex]]></abbrev-journal-title>
<issn>2306-4102</issn>
<publisher>
<publisher-name><![CDATA[Colegio Mexicano de Ortopedia y Traumatología A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2306-41022025000300152</article-id>
<article-id pub-id-type="doi">10.35366/119910</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Deep learning applications in orthopaedics: a systematic review and future directions]]></article-title>
<article-title xml:lang="es"><![CDATA[Aplicaciones de aprendizaje profundo en ortopedia: una revisión sistemática y futuras direcciones]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González-Pola]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Herrera-Lozano]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Graham-Nieto]]></surname>
<given-names><![CDATA[LF]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zermeño-García]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Centro Médico ABC Santa Fe Centro de Ortopedia y Traumatología ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Centro Médico ABC Santa Fe Hospital Español de México Departamento de Ortopedia]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Centro Médico ABC Santa Fe Hospital Ángeles Lomas Departamento de Ortopedia]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Centro Médico ABC Santa Fe Hospital Ángeles Lomas Departamento de Ortopedia]]></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>39</volume>
<numero>3</numero>
<fpage>152</fpage>
<lpage>163</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2306-41022025000300152&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2306-41022025000300152&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2306-41022025000300152&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract:  Introduction:  artificial intelligence and deep learning in orthopedics have gained mass interest in recent years. In prior studies, researchers have demonstrated different applications, from radiographic assessment to bone tumor diagnosis. The purpose of this review is to analyze the current literature on AI and deep learning tools to identify the most used tools in the risk assessment, outcome assessment, imaging, and basic science fields.  Material and methods:  searches were conducted in PubMed, EMBASE and Google Scholar from January 2020 up to October 31st, 2023. We identified 862 studies, 595 of which were included in the systematic review. A total of 281 studies about radiographic assessment, 102 about spine-oriented surgery, 95 about outcome assessment, 84 about fundamental AI orthopedic education, and 33 basic science applications were included. Primary outcomes were diagnostic accuracy, study design and reporting standards reported in the literature. Estimates were pooled using random effects meta-analysis.  Results:  53 different imaging methods were used to measure radiographic aspects. A total of 185 different machine learning algorithms were used, with the convolutional neural network architecture being the most common (73%). To improve diagnostic accuracy and speed were the most commonly achieved results (62%).  Conclusion:  heterogeneity was high among the studies, and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an overestimation of the diagnostic accuracy of DL algorithms for medical imaging. There is an immediate need for the development of artificial intelligence-specific guidelines to provide guidance around key issues in this field.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen:  Introducción:  la inteligencia artificial (IA) y deep learning en ortopedia han ganado un gran interés en los últimos años. En estudios anteriores, se han mostrado diferentes aplicaciones, desde la evaluación radiográfica hasta el diagnóstico de tumores óseos. El propósito de esta revisión es analizar literatura actual sobre IA y deep learning para identificar las herramientas más utilizadas en los campos de evaluación, resultados, imágenes y ciencias básicas.  Material y métodos:  se realizaron búsquedas en PubMed, EMBASE y Google Scholar desde enero de 2020 hasta el 31 de octubre de 2023. Se identificaron 862 estudios, de los cuales 595 fueron incluidos. Se incluyeron un total de 281 estudios sobre evaluación radiográfica, 102 sobre cirugía de columna, 95 sobre evaluación de resultados, 84 sobre educación ortopédica y 33 aplicaciones de ciencias básicas. Los resultados primarios fueron la precisión diagnóstica, diseño del estudio y estándares de presentación de informes en la literatura. Las estimaciones se agruparon mediante un metaanálisis de efectos aleatorios.  Resultados:  se utilizaron 53 métodos de imagen diferentes para medir los aspectos radiográficos. Se utilizaron un total de 185 algoritmos diferentes de aprendizaje automático, siendo la arquitectura de red neuronal convolucional la más común (73%). Mejorar la precisión y la velocidad del diagnóstico fueron los resultados más reportados (62%).  Conclusión:  la heterogeneidad fue alta entre los estudios y se observó una amplia variación en la metodología, terminología y medidas de resultados. Esto puede llevar a una sobreestimación de la precisión diagnóstica de los algoritmos para imagenología. Existe una necesidad inmediata de desarrollar directrices específicas para la IA que proporcionen orientación sobre cuestiones clave.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[deep learning]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[orthopedics]]></kwd>
<kwd lng="en"><![CDATA[convolutional network]]></kwd>
<kwd lng="en"><![CDATA[imaging]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje profundo]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[ortopedia]]></kwd>
<kwd lng="es"><![CDATA[red convolucional]]></kwd>
<kwd lng="es"><![CDATA[imagenología]]></kwd>
</kwd-group>
</article-meta>
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