Uma visão sobre a classificação de pneumonia viral e bacteriana por radiografias de tórax
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1341Palavras-chave:
Inteligência Artificial, Radiografias de tórax, PneumoniaResumo
Objetivo: Este estudo apresenta uma revisão sistemática sobre o uso de Inteligência Artificial (IA), especialmente Deep Learning (DL), no diagnóstico e classificação da pneumonia por radiografias de tórax (RXT). Método: O estudo segue o protocolo PRISMA conduzindo a revisão em fases de identificação, triagem e análise de artigos da base Scopus. Resultados: A revisão recuperou 25 artigos relevantes entre 121 retornados e identificou crescente interesse científico pelo tema, além de avanços no diagnóstico, com alguns estudos alcançando até 99,7% acurácia no modelo proposto. Conclusão: A detecção precoce da pneumonia é essencial para um tratamento mais eficaz, e soluções que auxiliem especialistas são fundamentais. A literatura mostra que há uma evolução constante dessas soluções, embora ainda existam gargalos importantes a serem resolvidos.
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