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Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs.

Lorenzo Bonicelli1, Abigail Rose Trachtman2, Alfonso Rosamilia3

  • 1AImageLab, University of Modena and Reggio Emilia, Via Vivarelli 10/1, 41125 Modena, Italy.

Animals : an Open Access Journal From MDPI
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can now identify and score enzootic pneumonia-like lesions in pigs using digital images from slaughterhouses. This automated method aids disease surveillance and food safety without disrupting the slaughter chain.

Keywords:
artificial intelligenceconvolutional neural networksdeep learningpigpneumoniascoring methodsslaughterhouse

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Area of Science:

  • Veterinary Medicine
  • Animal Health
  • Digital Pathology

Background:

  • Slaughterhouses are crucial for monitoring animal disease prevalence and economic impact.
  • Manual lesion scoring by veterinarians is labor-intensive and time-consuming.
  • Artificial intelligence (AI) offers potential for automating repetitive tasks in livestock production.

Purpose of the Study:

  • To develop an AI-based method for recognizing and quantifying enzootic pneumonia-like lesions.
  • To assess the feasibility of this AI method under routine abattoir conditions.
  • To evaluate the AI method's impact on food hygiene and carcass handling.

Main Methods:

  • Digital images of slaughtered pigs were captured under abattoir conditions.
  • An AI-based algorithm was developed to identify and score lung lesions.
  • The AI method's performance was evaluated for accuracy and efficiency.

Main Results:

  • The AI-based method successfully identified and scored enzootic pneumonia-like lesions.
  • The system operated effectively without interfering with the slaughter chain routine.
  • The AI approach aligns with European legislation by minimizing carcass handling.

Conclusions:

  • AI provides a viable tool for automated lesion scoring in high-throughput slaughterhouses.
  • This technology can enhance disease surveillance and economic impact assessment in swine.
  • The method contributes to improved food hygiene by reducing microbial contamination risks.