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Inteligencia artificial en el diagnóstico histopatológico de microorganismos

Artificial intelligence in the histopathological diagnosis of microorganisms




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González Coba , A. ., Victoria Caro, M. ., Romero Fandiño, I. A. ., Quintero, L. M. ., Mosquera-Zamudio, A. ., Polo Nieto, F. ., Sprockel Díaz, J. J. ., Gomez López, A. . ., & Parra Medina, R. . (2024). Inteligencia artificial en el diagnóstico histopatológico de microorganismos. Revista Repertorio De Medicina Y Cirugía, 33(3), 230-237. https://doi.org/10.31260/RepertMedCir.01217372.1585

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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.

Ivan Alberto Romero Fandiño
John Jaime Sprockel Díaz

Andrea González Coba ,

Residente III de Patología, Fundación Universitaria de Ciencias de la Salud. Bogotá DC.


María Victoria Caro,

Residente I de Patología, Fundación Universitaria de Ciencias de la Salud. Bogotá DC.


Ivan Alberto Romero Fandiño,

Residente I de Patología, Fundación Universitaria de Ciencias de la Salud, Bogotá DC.


Lina María Quintero,

Residente I de Patología, Fundación Universitaria de Ciencias de la Salud. Bogotá DC.


Andrés Mosquera-Zamudio,

Estudiante de doctorado, Universitat de València, Valencia-España.


Fernando Polo Nieto,

Instructor Asociado, Fundación Universitaria de Ciencias de la Salud. Bogotá DC, Colombia.


John Jaime Sprockel Díaz,

Docente de Medicina Interna, Fundación Universitaria de Ciencias de la Salud, Bogotá DC.


Arley Gomez López,

Médico PhD. Vicerrectoría de Investigaciones, Fundación Universitaria de Ciencias de la Salud. Bogotá DC, Colombia.


Rafael Parra Medina,

Médico Patólogo y Epidemiólogo, Fundación Universitaria de Ciencias de la Salud, Bogotá DC, Colombia. 


Introducción: la mayoría de las aplicaciones en patología digital se encuentran relacionadas con la oncológica, aunque se han propuesto algunos modelos recientes que permiten evaluar la utilidad en el diagnóstico histológico de microorganismos. Material y métodos: se realizó la siguiente revisión en la que se incluyeron 10 artículos publicados en inglés, que tienen como eje central el diagnóstico histopatológico de microorganismos y diferentes modelos de inteligencia artificial. Discusión: los diseñados se han probado para el diagnóstico de Helicobacter pylori, Mycobacterium tuberculosis, Aspergillus, Mucorales y microorganismos relacionados con onicomicosis. Conclusiones: se recomienda el uso de la inteligencia artificial en el diagnóstico histopatológico de microorganismos como un campo emergente que refuerza la función del patólogo coordinador de los diferentes modelos, optimizando así su función y mejorando los tiempos de trabajo y los niveles de efectividad. 


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