0026/2026 - Inteligência Artificial com Redes Neurais Convolucionais para Detecção de Microfilárias na Amazônia Brasileira Artificial Intelligence with Convolutional Neural Networks for Microfilariae Detection in the Brazilian Amazon
A Amazônia apresenta limitações estruturais persistentes para o diagnóstico das filarioses. Este estudo teve como objetivo desenvolver e avaliar um modelo de inteligência artificial baseado em redes neurais convolucionais para classificar imagens microscópicas quanto à presença ou ausência de microfilárias. Trata-se de um estudo tecnológico, quantitativo e aplicado, no qual amostras sanguíneas de 43 cães foram coletadas na zona rural de Manaus, preparadas em lâminas coradas e digitalizadas por webcam acoplada ao microscópio, gerando 500 imagens originais. As imagens foram pré-processadas, organizadas em classes binárias e submetidas a aumento de dados no conjunto de treinamento, resultando em aproximadamente 1.000 instâncias. O ground truth foi definido por avaliação morfológica especializada e confirmação molecular por microdissecação a laser e PCR. O modelo EfficientNetV2-B0, treinado em abordagem patch-based, alcançou acurácia de 93,6%, precisão de 91,8%, sensibilidade de 92,4% e F1-score de 92,1%. O tempo médio de análise por lâmina foi de 104 segundos com a IA, frente a 2.065 segundos na leitura humana, evidenciando ganho expressivo de eficiência e potencial de aplicação na triagem parasitológica e na vigilância epidemiológica em cenários de infraestrutura limitada.
Palavras-chave:
inteligência artificial; filariose; microfilárias; Amazônia; vigilância em saúde.
Abstract:
The Amazon region faces persistent structural limitations for the diagnosis of filarial diseases. This study aimed to develop and evaluate an artificial intelligence model based on convolutional neural networks to classify microscopic images according to the presence or absence of microfilariae. This was a technological, quantitative, and applied study in which blood samples from 43 dogs were collected in rural areas of Manaus, prepared on stained slides, and digitized using a webcam coupled to a microscope, generating 500 original images. The images were preprocessed, organized into binary classes, and subjected to data augmentation in the training set, resulting in approximately 1,000 instances. Ground truth was established through expert morphological assessment and molecular confirmation by laser microdissection and polymerase chain reaction (PCR). The EfficientNetV2-B0 model, trained using a patch-based approach, achieved an accuracy of 93.6%, precision of 91.8%, sensitivity of 92.4%, and an F1-score of 92.1%. The average analysis time per slide was 104 seconds using artificial intelligence, compared with 2,065 seconds for human reading, demonstrating a substantial gain in efficiency and highlighting the potential application of this approach in parasitological screening and epidemiological surveillance in settings with limited infrastructure.
Keywords:
artificial intelligence; filariasis; microfilariae; Amazon; health surveillance.
Artificial Intelligence with Convolutional Neural Networks for Microfilariae Detection in the Brazilian Amazon
Resumo (abstract):
The Amazon region faces persistent structural limitations for the diagnosis of filarial diseases. This study aimed to develop and evaluate an artificial intelligence model based on convolutional neural networks to classify microscopic images according to the presence or absence of microfilariae. This was a technological, quantitative, and applied study in which blood samples from 43 dogs were collected in rural areas of Manaus, prepared on stained slides, and digitized using a webcam coupled to a microscope, generating 500 original images. The images were preprocessed, organized into binary classes, and subjected to data augmentation in the training set, resulting in approximately 1,000 instances. Ground truth was established through expert morphological assessment and molecular confirmation by laser microdissection and polymerase chain reaction (PCR). The EfficientNetV2-B0 model, trained using a patch-based approach, achieved an accuracy of 93.6%, precision of 91.8%, sensitivity of 92.4%, and an F1-score of 92.1%. The average analysis time per slide was 104 seconds using artificial intelligence, compared with 2,065 seconds for human reading, demonstrating a substantial gain in efficiency and highlighting the potential application of this approach in parasitological screening and epidemiological surveillance in settings with limited infrastructure.
Palavras-chave (keywords):
artificial intelligence; filariasis; microfilariae; Amazon; health surveillance.
