Uma nova abordagem de padrões binários em radiografias de tórax para avançar o diagnóstico de tuberculose
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1349Palavras-chave:
Diagnóstico, Inteligência Artificial, TuberculoseResumo
Objetivo: A tuberculose (TB) afeta milhões de pessoas, principalmente as mais miseráveis, revelando desigualdades sociais. Apesar dos avanços da inteligência artificial (IA) no controle da TB, poucos benefícios chegam aos mais necessitados. Este estudo propõe uma IA otimizada para discriminar casos de TB de indivíduos saudáveis. Método: A abordagem incorpora descritores por congruência de fase e padrões binários locais em um modelo de otimização mínima sequencial (SMO) na análise de radiografias de tórax (RXT). Resultados: A IA otimizada apresenta desempenho superior a abordagens existentes na literatura, entregando valor de especificidade superior a 97% em diferentes bases e cenários de segmentação. Conclusão: A aplicação da IA proposta na análise de RXT pode representar um avanço significativo no controle da TB, especialmente em populações mais necessitadas, pois constitui uma solução acessível e eficaz que abre possibilidades para o desenvolvimento de novos sistemas de apoio ao diagnóstico.
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