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Monitoring of Respiratory Disease Patterns in a Multimicrobially Infected Pig Population Using Artificial
Matthias Eddicks1, Franziska Feicht1, Jochen Beckjunker2
1Clinic for Swine at the Centre for Clinical Veterinary Medicine, Ludwig-Maximilians-University München, 85764 München, Germany.
Viruses
|October 26, 2024
Summary
AI-powered cough monitoring combined with oral fluid and bioaerosol screening effectively identifies respiratory pathogens in pigs. This integrated approach aids in understanding the causes of respiratory distress in swine populations.
Area of Science:
- Veterinary Medicine
- Animal Health
- Infectious Disease Epidemiology
Background:
- Respiratory diseases pose significant challenges in conventional pig nurseries, impacting animal welfare and economic viability.
- Multimicrobial infections complicate the diagnosis and management of respiratory distress in swine populations.
- Current diagnostic methods may not provide continuous, real-time monitoring of respiratory health.
Purpose of the Study:
- To evaluate the added value of a 24/7 AI sound-based coughing monitoring system for identifying respiratory disease patterns.
- To correlate AI-derived respiratory health data with molecular diagnostics for etiological investigation of respiratory distress.
- To compare the efficacy of oral fluids (OFs) and bioaerosol (AS) sampling for pathogen detection in pigs.
Main Methods:
- A 24/7 AI sound-based coughing monitoring system was implemented in a pig nursery.
- Respiratory distress was continuously measured by AI and compared to human observations.
- Swine influenza A virus (swIAV), porcine reproductive and respiratory disease virus (PRRSV), *Mycoplasma hyopneumoniae*, *Actinobacillus pleuropneumoniae*, and porcine circovirus 2 (PCV2) were screened using qPCR in OFs and AS.
Main Results:
- Most targeted pathogens, except *M. hyopneumoniae*, were detected during the study period.
- High swIAV-RNA loads in OFs and AS correlated significantly with decreased respiratory health scores from the AI.
- Oral fluids (OFs) showed significantly higher odds and lower Ct-values for detecting PRRSV and *A. pleuropneumoniae* compared to bioaerosols (AS).
Conclusions:
- AI-based cough monitoring, when integrated with laboratory diagnostics, serves as an effective early warning system for respiratory diseases in pigs.
- This combined approach provides valuable insights into the etiology of respiratory distress in multimicrobially infected pig populations.
- Oral fluid sampling demonstrates superior sensitivity for detecting specific respiratory pathogens like PRRSV and *A. pleuropneumoniae* compared to bioaerosol sampling.
