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News Article

Machine learning could improve the diagnosis of mastitis

Artificial intelligence could help vets to more accurately diagnose the origin of mastitis on dairy herds

A study published in Scientific Reports has found that machine learning has the potential to enhance and improve a veterinarian’s ability to accurately diagnose herd mastitis origin and reduce mastitis levels on dairy farms.

A crucial first step in the control of mastitis is identifying where mastitis-causing pathogens originate; does the pathogen come from the cows’ environment or is it contagiously spread through the milking parlour? This diagnosis is usually performed by a veterinarian by analysing data from the dairy farm, however this requires both time and specialist veterinary training.

This study, which was led by veterinarian and researcher Robert Hyde from the School of Veterinary Medicine and Science at the University of Nottingham, aims to create an automated diagnostic support tool for the diagnosis of herd level mastitis origin.

Mastitis data from 1,000 UK dairy herds was inputted for several three-month periods. Machine learning algorithms were used to classify herd mastitis origin and compared with expert diagnosis by a specialist veterinarian.

The machine learning algorithms were able to achieve a classification accuracy of 98% for environmental vs contagious mastitis, and 78% accuracy was achieved for the classification of lactation vs dry period environmental mastitis when compared with expert veterinary diagnosis.

The researchers say: “an accurate, automated mastitis diagnosis tool has great potential to aid non-specialist veterinary clinicians to make a rapid herd level diagnosis and promptly implement appropriate control measures.”

Article: Hyde, R.M., Down, P.M., Bradley, A.J. Breen, J. E., Hudson, C., Leach, K. A., Green, M. J. (2020). Automated prediction of mastitis infection patterns in dairy herds using machine learning. Scientific Reports, volume 10, Article number: 4289, doi: 10.1038/s41598-020-61126-8

Article details

  • Date
  • 10 March 2020
  • Source
  • University of Nottingham
  • Subject(s)
  • Food Animals