Un nuevo enfoque de patrones binarios en las radiografías de tórax para avanzar en el diagnóstico de la tuberculosis
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1349Palabras clave:
Diagnóstico, Inteligencia artificial, TuberculosisResumen
Objetivo: La tuberculosis (TB) afecta a millones de personas, especialmente a las más miserables, revelando desigualdades sociales. A pesar de los avances en inteligencia artificial (IA) en el control de la tuberculosis, pocos beneficios llegan a quienes más los necesitan. Este estudio propone una IA optimizada para discriminar los casos de tuberculosis de los individuos sanos. Método: el enfoque incorpora descriptores de congruencia de fase y patrones binarios locales en un modelo de optimización mínima secuencial (SMO) para analizar radiografías de tórax (CXR). Resultados: La IA optimizada funciona mejor que los enfoques existentes en la literatura, entregando un valor de especificidad superior al 97% en diferentes bases y escenarios de segmentación. Conclusión: La aplicación de la IA propuesta en el análisis RXT podría representar un avance significativo en el control de la tuberculosis, especialmente en las poblaciones más necesitadas, ya que constituye una solución accesible y eficaz que abre posibilidades para el desarrollo de nuevos sistemas de apoyo al diagnóstico.
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