Revisión Sistemática de Literatura: Análisis de viabilidad para la detección y diagnóstico de Covid-19, aplicando modelos de Inteligencia Artificial (IA)
DOI:
https://doi.org/10.54753/cedamaz.v11i2.1183Palabras clave:
Diagnóstico, Covid-19, CNN, VGG16, Radiografías pulmonares, Rayos X.Resumen
Desde la declaración de la emergencia sanitaria provocada por el Covid-19 en marzo del 2020, hasta la fecha, existen aproximadamente 219 millones de contagiados, de los cuales 4,5 millones han muerto. En nuestro país, se estima que existen 508 mil casos confirmados y aproximadamente 32 mil muertes a causa de esta enfermedad. Pese a disponer de métodos verificados para diagnosticar Covid-19, las pruebas Polymerase Chain Reaction (PCR) o Real Time-PCR (RT-PCR), tienden a generar falsos positivos y negativos entre el 30\% y el 40\%. Por tal razón, ayudar a los métodos tradicionales a realizar un diagnóstico clínico preciso, usando como datos de entrada radiografías pulmonares, supone un cambio radical en la detección de Covid-19, puesto que, es una alternativa mucho más cómoda para el paciente y lo que es más importante, aumenta el nivel de precisión reduciendo a la vez, las tasas de falsos positivos y negativos. En la presente Revisión Sistemática de Literatura (RSL), la cual se ha basado en la metodología de Bárbara Kitchenham, busca sustentar la creación de un modelo basado en la arquitectura de Redes Neuronales Convolucionales (CNN), capaz de analizar radiografías pulmonares para el diagnóstico de Covid-19. Como resultado, se pudo dar contestación a las tres preguntas de investigación planteadas, mismas que sirvieron para delimitar el presente estudio, para ello se analizó 41 trabajos relacionados (TR), los cuales se enfocaban en diferentes métodos de diagnóstico basados en Inteligencia Artificial (IA), no obstante 16 de estos TR hacían referencia al uso de CNN para el diagnóstico de Covid-19 mediante el análisis de tomografías computarizadas (TC) y radiografías pulmonares (Rayos X), siendo esta última la opción más viable para aplicarlo en nuestro entorno, debido la disponibilidad de datos. Además, el uso de recursos por parte de estos métodos es asequible tanto a nivel local, usando la Unidad de Procesamiento Gráfico (GPU) Nvidia y memoria RAM superior a 8GB como base, o utilizar procesamiento en la nube usando Google Colab.Métricas
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