Scientists have developed a new artificial intelligence system that can determine if an animal is in pain by analysing its expressions.
The system, developed by researchers at the University of Cambridge in the UK, uses five different facial expressions to estimate the severity of pain in sheep.
The results could be applied to other types of animals, such as rodents used in animal research, rabbits or horses, researchers said.
The system is able to detect the distinct parts of a sheep's face and compare it with a standardised measurement tool developed by veterinarians for diagnosing pain.
Severe pain in sheep is associated with conditions such as foot rot, an extremely painful and contagious condition which causes the foot to rot away; or mastitis, an inflammation of the udder in ewes caused by injury or bacterial infection.
Both of these conditions are common in large flocks, and early detection will lead to faster treatment and pain relief.
Reliable and efficient pain assessment would also help with early diagnosis.
The Sheep Pain Facial Expression Scale (SPFES) is a tool to measure pain levels based on facial expressions of sheep, and has been shown to recognise pain with high accuracy.
However, training people to use the tool can be time- consuming and individual bias can lead to inconsistent scores.
In order to make the process of pain detection more accurate, researchers used the SPFES as the basis of an AI system which uses machine learning techniques to estimate pain levels in sheep.
To train the AI sytem, researchers used a dataset of about 500 photographs of sheep, which had been gathered by veterinarians in the course of providing treatment.
They labelled different parts of the sheep's faces on each photograph and ranked the pain levels of the animals according to SPFES.
Early tests of the model showed that it was able to estimate pain levels with about 80 per cent degree of accuracy.
While the results with still photographs have been successful, in order to make the system more robust, they require much larger datasets, researchers said.
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