The work in question was carried out by researchers at the Massachusetts Institute of Technology (MIT). They developed an artificial intelligence that is able to predict how much pain a person is in by looking at an image. The system, called “DeepFaceLIFT,” is a machine-learning algorithm that was trained on videos of people wincing or showing other signs of discomfort. From these videos, it was able to learn the different subtleties in a person’s facial micro-expressions that would help estimate the level of pain they are in, when taken in association with self-reported pain scores. The algorithm can be honed according to a person’s age, sex, and skin complexion, and turns out to be a lot more accurate than previous one-size-fits-all research projects.
MIT’s machine-learning project might sound sadistic but it has useful potential real-world applications. At present, the so-called “gold standard for pain measurement” is something called the visual-analog scale (VAS) pain metric. While useful, this VAS metric is entirely self-reported, which makes it both subjective and context-dependent, and its range can vary significantly between different people. An algorithm is unlikely to ever totally replace these kinds of self-reported systems for a variety of reasons (imagine telling a patient in hospital that you’re denying medication because the computer says they are not exhibiting the right pained expression!), but it could be a useful clinical tool in the quest to make pain reporting more objective. It may be especially valuable in determining whether or not a person is being honest about their pain levels — and not faking it.
There is still work to be done on the project, but the hope is to eventually develop it into a mobile app that could be accessed by physicians.
A paper describing the project was recently published in the Journal of Machine Learning Research.
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