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A Non-Invasive Diagnostic Platform for Canine Leishmaniasis Using VOC Analysis and Distributed Veterinary

Marius Iulian Mihailescu1, Violeta Elena Simion2, Alexandra Ursachi2

  • 1Faculty of Engineering and Computer Science, SPIRU HARET University, 47 Fabricii Street, 076144 Bucharest, Romania.

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Summary

This study introduces a novel software architecture for non-invasive canine leishmaniasis detection using AI and gas sensors to identify disease biomarkers in dog breath and hair. Early parasite detection aids diagnosis, treatment, and transmission prevention.

Keywords:
artificial intelligence in animal healthcanine leishmaniasiscloud-based architecturemachine learningnon-invasive testingveterinary diagnosticsvolatile organic compounds (VOCs)

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Area of Science:

  • Veterinary Medicine
  • Computational Biology
  • Environmental Science

Background:

  • Canine leishmaniasis poses a significant health risk to dogs and can be transmitted to humans.
  • Current diagnostic methods can be invasive and may not detect the disease in its early stages.
  • Identifying disease-specific volatile organic compounds (VOCs) offers a potential non-invasive diagnostic approach.

Purpose of the Study:

  • To develop and describe a new software architecture for the non-invasive detection of canine leishmaniasis.
  • To integrate gas-sensing technologies, artificial intelligence (AI), and cloud-based software for disease identification.
  • To enable early detection of parasites at all infectious stages.

Main Methods:

  • A multi-tier software architecture was designed, incorporating data collection, pre-processing, and machine learning (ML) analysis.
  • Gas-sensing technologies were employed to detect VOCs in canine breath and hair samples.
  • The platform was integrated into a website plug-in, featuring user interfaces for veterinarians, researchers, and pet owners.

Main Results:

  • The proposed platform successfully integrates diverse components for canine leishmaniasis detection.
  • The system is designed to identify disease-specific VOCs, facilitating non-invasive diagnosis.
  • The architecture supports various user roles, enhancing accessibility and utility.

Conclusions:

  • The developed software architecture provides a robust framework for non-invasive canine leishmaniasis detection.
  • Early identification of infectious parasites is crucial for effective disease management and control.
  • This AI-powered, sensor-based approach represents a significant advancement in veterinary diagnostics.