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🧑🏼‍💻 Research - October 28, 2024

Monitoring of Respiratory Disease Patterns in a Multimicrobially Infected Pig Population Using Artificial Intelligence and Aggregate Samples.

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⚡ Quick Summary

This study implemented a 24/7 AI sound-based coughing monitoring system alongside molecular diagnostics to assess respiratory disease patterns in a multimicrobially infected pig population. The findings revealed that high swIAV-RNA loads were significantly associated with decreased respiratory health, highlighting the potential of AI in veterinary diagnostics. 🐖

🔍 Key Details

  • 📊 Study Population: Conventional pig nursery with multimicrobially infected pigs
  • 🧬 Pathogens Screened: swine influenza A virus (swIAV), porcine reproductive and respiratory disease virus (PRRSV), Mycoplasma hyopneumoniae, Actinobacillus pleuropneumoniae, and porcine circovirus 2 (PCV2)
  • ⚙️ Technology Used: AI sound-based monitoring and qPCR diagnostics
  • 📅 Study Duration: Continuous monitoring over the study period

🔑 Key Takeaways

  • 🤖 AI technology provided continuous monitoring of respiratory distress in pigs.
  • 🔬 Molecular diagnostics were used to identify multiple respiratory pathogens.
  • 📉 High swIAV-RNA loads correlated with lower respiratory health scores.
  • 📊 Odds of detecting PRRSV and A. pleuropneumoniae were higher in oral fluids compared to bioaerosols.
  • 🔍 qPCR results showed lower Ct-values for swIAV and A. pleuropneumoniae in oral fluids.
  • ⚠️ AI serves as an early warning system for respiratory health issues in pigs.
  • 🌱 Study contributes to understanding the etiology of respiratory distress in livestock.

📚 Background

Respiratory diseases in pigs can lead to significant economic losses in the swine industry. Traditional monitoring methods often rely on human observations, which can be subjective and inconsistent. The integration of artificial intelligence and molecular diagnostics offers a promising approach to enhance disease monitoring and management in livestock populations, potentially leading to improved animal health and welfare.

🗒️ Study

The study was conducted in a conventional pig nursery, where a 24/7 AI sound-based coughing monitoring system was deployed. This system continuously assessed respiratory distress and was complemented by molecular diagnostics using oral fluids and bioaerosol samples to screen for various respiratory pathogens. The aim was to evaluate the effectiveness of AI in identifying disease patterns and understanding the underlying causes of respiratory distress in pigs.

📈 Results

The results indicated that all pathogens, except for Mycoplasma hyopneumoniae, were detected during the study period. Notably, high levels of swIAV-RNA in oral fluids and bioaerosols were significantly linked to a decline in respiratory health, as measured by the AI-generated respiratory health score. Furthermore, the odds of detecting PRRSV and A. pleuropneumoniae were significantly higher in oral fluids, and qPCR examinations revealed lower Ct-values for swIAV and A. pleuropneumoniae in these samples compared to bioaerosols.

🌍 Impact and Implications

The findings from this study have significant implications for the swine industry. By utilizing AI for continuous monitoring and combining it with molecular diagnostics, farmers and veterinarians can gain valuable insights into respiratory health trends and pathogen prevalence. This approach not only enhances early detection of respiratory diseases but also aids in formulating targeted interventions, ultimately improving animal welfare and reducing economic losses in pig farming.

🔮 Conclusion

This study highlights the transformative potential of artificial intelligence in monitoring respiratory diseases in livestock. The integration of AI with molecular diagnostics can lead to more accurate and timely assessments of animal health, paving the way for improved management practices in the swine industry. Continued research in this area is essential to further refine these technologies and enhance their application in veterinary medicine.

💬 Your comments

What are your thoughts on the use of AI in monitoring animal health? We would love to hear your insights! 💬 Share your comments below or connect with us on social media:

Monitoring of Respiratory Disease Patterns in a Multimicrobially Infected Pig Population Using Artificial Intelligence and Aggregate Samples.

Abstract

A 24/7 AI sound-based coughing monitoring system was applied in combination with oral fluids (OFs) and bioaerosol (AS)-based screening for respiratory pathogens in a conventional pig nursery. The objective was to assess the additional value of the AI to identify disease patterns in association with molecular diagnostics to gain information on the etiology of respiratory distress in a multimicrobially infected pig population. Respiratory distress was measured 24/7 by the AI and compared to human observations. Screening for swine influenza A virus (swIAV), porcine reproductive and respiratory disease virus (PRRSV), Mycoplasma (M.) hyopneumoniae, Actinobacillus (A.) pleuropneumoniae, and porcine circovirus 2 (PCV2) was conducted using qPCR. Except for M. hyopneumoniae, all of the investigated pathogens were detected within the study period. High swIAV-RNA loads in OFs and AS were significantly associated with a decrease in respiratory health, expressed by a respiratory health score calculated by the AI The odds of detecting PRRSV or A. pleuropneumoniae were significantly higher for OFs compared to AS. qPCR examinations of OFs revealed significantly lower Ct-values for swIAV and A. pleuropneumoniae compared to AS. In addition to acting as an early warning system, AI gained respiratory health data combined with laboratory diagnostics, can indicate the etiology of respiratory distress.

Author: [‘Eddicks M’, ‘Feicht F’, ‘Beckjunker J’, ‘Genzow M’, ‘Alonso C’, ‘Reese S’, ‘Ritzmann M’, ‘Stadler J’]

Journal: Viruses

Citation: Eddicks M, et al. Monitoring of Respiratory Disease Patterns in a Multimicrobially Infected Pig Population Using Artificial Intelligence and Aggregate Samples. Monitoring of Respiratory Disease Patterns in a Multimicrobially Infected Pig Population Using Artificial Intelligence and Aggregate Samples. 2024; 16:(unknown pages). doi: 10.3390/v16101575

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