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Arboviral Encephalitis01:25

Arboviral Encephalitis

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Arboviral encephalitis refers to brain inflammation caused by arthropod-borne viruses, particularly those transmitted through mosquito vectors. Among these, West Nile virus (WNV), a member of the Flaviviridae family, is a significant public health concern. WNV is an enveloped, positive-sense, single-stranded RNA virus. Human infection typically begins when an infected mosquito introduces the virus into the dermis during feeding. The primary transmission cycle involves birds as amplifying hosts...
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Automated identification of spotted-fever tick vectors using convolutional neural networks.

Isadora R C Gomes1,2, Vinícius L Miranda1,2, José Fabrício C Leal1,2

  • 1Programa de Pós-graduação em Medicina Tropical, Núcleo de Medicina Tropical, Faculdade de Medicina, Universidade de Brasília, Brasília, Brazil.

Medical and Veterinary Entomology
|July 4, 2025
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Summary

Artificial intelligence using Convolutional Neural Networks (CNNs) can accurately identify tick species that transmit spotted fever (SF) in South America. This technology aids in public health surveillance and citizen science efforts for tick control.

Keywords:
Amblyommaartificial intelligencehealth surveillancemachine learningone healthtick‐borne pathogens

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Area of Science:

  • Veterinary Entomology
  • Medical Entomology
  • Bioinformatics

Background:

  • Ticks are significant ectoparasites and vectors for pathogens affecting humans and animals, posing a One Health challenge.
  • Rickettsia rickettsii and Rickettsia parkeri cause tick-borne spotted fever (SF) in South America, transmitted by Amblyomma species.
  • Machine learning, a subset of artificial intelligence, shows promise for automating image identification tasks in biological and medical research.

Purpose of the Study:

  • To evaluate the efficacy of Convolutional Neural Networks (CNNs) - AlexNet, ResNet-50, and MobileNetV2 - in identifying tick species that transmit SF bioagents.
  • To assess the performance of these CNN models across various image datasets including sex, position, and resolution variations.
  • To determine the potential of AI in supporting public health surveillance and citizen science for tick-borne disease control.

Main Methods:

  • An image database was curated comprising various tick groups: females, males, dorsal/ventral views, and low/high resolutions.
  • The database included key SF vectors (Amblyomma aureolatum, A. ovale, A. sculptum), potential vectors (A. triste, A. dubitatum), and a morphologically similar non-vector (A. cajennense s.s.).
  • CNN models (AlexNet, ResNet-50, MobileNetV2) were trained and evaluated using accuracy, sensitivity, and specificity metrics. Grad-CAM was employed for result interpretability.

Main Results:

  • CNNs achieved approximately 90% accuracy in tick identification.
  • Sensitivity ranged from 59% to 100%, varying by species, sex, position, and image resolution.
  • AlexNet and MobileNetV2 demonstrated superior sensitivity and specificity when all image types were considered for identifying SF vectors.

Conclusions:

  • CNNs show significant potential for the automated identification of tick species transmitting SF bioagents in South America.
  • The developed image database can facilitate the creation of tick identification applications for public health.
  • AI-driven tick identification can enhance disease surveillance and contribute to citizen science initiatives for tick management.