Artificial Intelligence with Convolutional Neural Networks for Microfilariae Detection in the Brazilian Amazon

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

Amazon rainforest · Artificial neural network · Convolutional neural network · Ground truth · Laser capture microdissection · Pattern recognition (psychology) · Sensitivity (control systems) · Digital Imaging for Blood Diseases · Parasites and Host Interactions · Parasitic Diseases Research and Treatment

• High-performance medicine

  Open Access•Eric J Topol•Nature Medicine•2019

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