ArticleVeterinary sciences2025
Machine Learning-Based Detection of Pig Coughs and Their Association with Respiratory Diseases in Fattening Pigs.
Article in Veterinary sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Exploratory Room-Level Acoustic Soundscape Monitoring of Cough-like Events Under Standard and Ventilation-Restricted Pig-Housing Conditions Using Audio Spectrogram Transformer.Animals : an open access journal from MDPI · 2026Article
- Pilot Room-Level Acoustic and Physiological Monitoring of Respiratory Disturbance in Pigs Following ExperimentalVeterinary sciences · 2026Article
- AI-Based Respiratory Monitoring-Guided Evaluation of Rottlerin Therapy for PRRS in Grower-Finisher Pig Farms.Viruses · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Respiratory infections are a major concern in pig farming as they negatively impact animal health and productivity. Coughing is a key symptom of respiratory disease and can be classified as productive or non-productive, but human assessment often leads to inconsistencies. This study aimed to use a machine learning model to classify pig coughs and investigate their association with respiratory infections. Cough sounds from 49 fattening pigs were recorded and analyzed using a Python-based machine learning system. The model's accuracy in detecting coughs was 0.72, compared to 0.69 for farmers. For classification of non-productive coughs, the machine learning results showed strong agreement with infection status by
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Registered trials
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