The media is abuzz with the subject of chronic pain and the corresponding rise in prescription pain killer abuse. The issue affects millions of people around the world, exacting a tremendous cost on health care costs and lost productivity, and imposing great emotional and financial tolls on those who suffer it. Now research using artificial intelligence to diagnose pain in sheep, may yield more data on our own forms of pain.

Artificial Intelligence  

Human beings are not the only ones who battle pain daily – so, too, do animals, who often fail to express pain in the way we would expect.

Sheep, for instance, often fall prey to infections and disease, yet to the untrained human eye, everything may seem normal. Just a few of the painful conditions they suffer in silence include polyarthritis (which affects the leg joints), foot rot (a very painful condition causing the foot to rot away), mastitis (an inflammation of udders in ewes resulting from infection or injury) and uterine infections. In the case of foot rot, for instance, a sheep will not show the characteristic symptoms (which include lameness) until the disease is quite advanced. This results in unnecessary pain for affected sheep, but also in longer treatments that may not produce the desired outcome if the disease has reached very advanced stages.

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Artificial intelligence could be a major lifesaver for sheep and other animals undergoing unnecessary pain because of the difficulty of detection, thanks to the work of computer scientists at the University of Cambridge. Their research, presented at a conference in Washington, showed that the well being of sheep (and other animals) could greatly be improved by the early diagnosis and treatment of painful conditions via AI.

The new system detects different parts of a sheep’s face, comparing the face with a standardized measuring tool created by veterinary scientists to diagnose pain. In 2016, another University of Cambridge scientist, Dr. Krista McLennan had developed a useful pain level test called the Sheep Pain Facial Expression Scale (SPFES). The latter enabled sheep farmers to recognize pain by analyzing the facial expression of sheep, yet training people to use the scale properly was time-consuming, and the reliance on individual perceptions meant that accuracy could suffer from case to case.

The newly developed system relied on the SPFES to come up with an AI system which relies on machine learning techniques to provide pain estimates in sheep. Previously, AI had been used to analyse expressions in humans, but this is the first time the technology has been tested on animals. Lead researcher, Dr. Peter Robinson, noted: “A lot of the earlier work on the faces of animals was actually done by Darwin, who argued that all humans and many animals show emotion through remarkably similar behaviors, so we thought there would likely be crossover between animals and our work in human faces.”

The SPFES defines five major changes which occur in sheep when they are in pain: their eyes become narrower, their cheek muscles tighten, their ears flop forwards, their lips are stretched down and back, and their nostrils (which are normally U-shaped) take on a V-shape. The SPFES then ranks the severity of pain depending on the extent of each changed characteristic. Interestingly, the researchers noted that some of these facial changes are also observed in human beings in pain – in particular, the muscles in our cheeks tend to tighten and our eyes become narrower.

The scientists used around 500 photographs of sheep receiving treatment for pain related conditions, to train the AI model by labelling the different parts of the faces in each photograph, ranking pain according to the SPFES. Results showed that the AI model was able to estimate pain levels with a remarkable 80 per cent accuracy. The next step for researchers is to train the model to recognise faces through moving images, and to train it to identify the sheeps’ faces even when they are not directly facing the camera. After all, in real life, sheep do not ‘pose’ for cameras and in order to be useful, any AI system would have to be able to predict disease by following animals in normal movement. Once the system was refined, it could be a very useful tool for farmers, who could take the affected sheep to receive a diagnosis and early treatment from their veterinarian.

By  Gemma Rogers	Embed
Author Bio - Prior to starting her career as a freelancer, Gemma worked for many years in business and finance. When she became a mother, she decided to turn to writing to make a living and now pens articles on as many different topics- from news and current affairs through to pieces on money matters.

 Computer Vision  

A joint study by researchers at the University of California, San Diego and the University of Toronto has found that a computer system spots real or faked expressions of pain more accurately than people can.

A joint study by researchers at the University of California, San Diego and the University of Toronto has found that a computer system spots real or faked expressions of pain more accurately than people can.

The work, titled “Automatic Decoding of Deceptive Pain Expressions,” is published in the latest issue of Current Biology.

“The computer system managed to detect distinctive dynamic features of facial expressions that people missed,” said Marian Bartlett, research professor at UC San Diego’s Institute for Neural Computation and lead author of the study. “Human observers just aren’t very good at telling real from faked expressions of pain.”

"Our computer-vision system can be applied to detect states in which the human face may provide important clues as to health, physiology, emotion, or thought."

Senior author Kang Lee, professor at the Dr. Eric Jackman Institute of Child Study at the University of Toronto, said “humans can simulate facial expressions and fake emotions well enough to deceive most observers. The computer’s pattern-recognition abilities prove better at telling whether pain is real or faked.”

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The research team found that humans could not discriminate real from faked expressions of pain better than random chance – and, even after training, only improved accuracy to a modest 55 percent. The computer system attains an 85 percent accuracy.

“In highly social species such as humans,” said Lee, “faces have evolved to convey rich information, including expressions of emotion and pain. And, because of the way our brains are built, people can simulate emotions they’re not actually experiencing – so successfully that they fool other people. The computer is much better at spotting the subtle differences between involuntary and voluntary facial movements.”

“By revealing the dynamics of facial action through machine vision systems,” said Bartlett, “our approach has the potential to elucidate ‘behavioral fingerprints’ of the neural-control systems involved in emotional signaling.”

The single most predictive feature of falsified expressions, the study shows, is the mouth, and how and when it opens. Fakers’ mouths open with less variation and too regularly.

“Further investigations,” said the researchers, “will explore whether over-regularity is a general feature of fake expressions.”

In addition to detecting pain malingering, the computer-vision system might be used to detect other real-world deceptive actions in the realms of homeland security, psychopathology, job screening, medicine, and law, said Bartlett.

“As with causes of pain, these scenarios also generate strong emotions, along with attempts to minimize, mask, and fake such emotions, which may involve ‘dual control’ of the face,” she said. “In addition, our computer-vision system can be applied to detect states in which the human face may provide important clues as to health, physiology, emotion, or thought, such as drivers’ expressions of sleepiness, students’ expressions of attention and comprehension of lectures, or responses to treatment of affective disorders.”


SOURCE  UC San Diego

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