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Artificial intelligence in the histopathological diagnosis of microorganisms

Inteligencia artificial en el diagnóstico histopatológico de microorganismos




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Review Articles

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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). Artificial intelligence in the histopathological diagnosis of microorganisms. Journal of Medicine and Surgery Repertoire, 33(3), 230-237. https://doi.org/10.31260/RepertMedCir.01217372.1585

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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. 


Introduction: most of the digital pathology applications are related to oncology, although some recent models have been proposed to evaluate their usefulness in the histopathological diagnosis of microorganisms. Material and Methods: this review included 10 articles published in English, centered around the histopathological diagnosis of microorganisms and the different artificial intelligence (AI) models. Discussion: the designed AI models have been tested for diagnosing Helicobacter pylori, Mycobacterium tuberculosis, Aspergillus, Mucorales and microorganisms causing onychomycosis. Conclusions: the use of artificial intelligence in the histopathological diagnosis of microorganisms is recommended as an emerging field which assists the pathologist coordinating the different models, thus optimizing his function, and improving workflows and effectiveness levels.  


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