Machine learning has the potential to enhance and improve a veterinarian's ability to accurately diagnose a herd's mastitis origin and reduce mastitis levels on dairy farms, according to a new study published March 9 in Scientific Reports.

Mastitis is an extremely costly endemic disease of dairy cattle, and a crucial first step in the control of mastitis is identifying where mastitis-causing pathogens originate. For example, the University of Nottingham said in an announcement, does the bacteria come from the cows' environment, or is it contagiously spread through the milking parlor?.

This diagnosis is usually performed by a veterinarian by analyzing data from the dairy farm and, in the U.K., is a central part of the Agriculture & Horticulture Development Board's (AHDB) mastitis control plan, but this requires both time and specialist veterinary training.

According to the University of Nottingham, machine learning algorithms are widely used, from filtering spam emails and making Netflix movie suggestions to accurately classifying skin cancer. These algorithms approach diagnostic problems as a student doctor or veterinarian might -- by learning rules from data and applying them to new patients.

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

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

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

"Mastitis is a huge problem for dairy farmers, both economically and in welfare terms. In our study, we have shown that machine learning algorithms can accurately diagnose the origin of this condition on dairy farms. A diagnostic tool of this kind has great potential in the industry to tackle this condition and to assist veterinary clinicians in making a rapid diagnosis of mastitis origin at herd level in order to promptly implement control measures for an extremely damaging disease in terms of animal health, productivity, welfare and antimicrobial use," Hyde said.