Pain recognition tool a game changer
“A smartphone app that uses facial recognition to automatically detect pain in non-communicative people with dementia could revolutionise care and dramatically improve quality of life.
The Electronic Pain Assessment Tool (ePAT) being developed by researchers at Curtin University uses the mobile phone’s camera and facial recognition software to automate the facial recognition component of pain assessment.
The results are combined with the information collected on the other accepted pain indicators – vocalisation, behavioural change, psychological change, physiological change and physical change — to provide a total pain score.
By automating the facial recognition component, the tool aims to provide an objective way to detect an accurate level of pain in non-communicative people with dementia, said the tool’s lead developer, Professor Jeff Hughes from Curtin’s School of Pharmacy.
“We think it could be an absolute game changer and set the gold standard in that there should be an objective way in which to determine the presence of pain in people who have communication difficultly,” Prof Hughes said.
“The tool is extremely quick. The results that you get will be reproducible. You will be able to collect the pattern of the patient’s pain over time.”
Prof Hughes said the tool will first be able to detect and quantify pain, after which it can be used to re-score the patient to determine the effectiveness of the analgesic given.
How it works
The app records a five-to-10 second video of the person’s face using the smartphone’s camera. Looking at nine features of the face it locates markers to determine which of the particular indicators of pain are there, Prof Hughes explained.
The video is taken, utilised but not stored, and as everything is done in real time it is not a privacy risk, he said.
The tool then takes the user through the remaining six domains of pain indication where they input the information using the phone’s touch screen.
“At the end it gives you what it estimates as the final score and then it gives you a severity scale,” Prof Hughes said. “Once you get familiar with it, it would take less a minute to do the whole consultation.”
If we can guarantee whether a patient is in pain or not and can prove the objectivity of using ePAT we can ensure that everyone who has pain will be treated, he said.
Better pain management will improve a person’s behaviour and cognition, making them easier to be cared for, he said. “It will have an enormous impact on the quality of life for patients with dementia and importantly it will also have an impact on the quality of life of carers.”
As the tool is “extremely” easy to use, it will be suitable, and empowering, for family carers as well professional carers and health professionals, he said.”
I haven’t ever faced the challenge of working with people who are unable to communicate their pain. This seems to be an exciting step in the right direction. Of course the risk is if the App gets it wrong; false positives or negatives because people are just, you know, different in how they experience and express pain or not.
I’d beg to differ on one point though “Prof Hughes said the tool will first be able to detect and quantify pain…”
‘Pain’ is not being detected nor quantified here, instead an individuals outward behaviour is being detected and quantified with the assumption that the outward behaviour somehow relates to a phenomenological experience. I think this is not a trivial point and one worth keeping in mind. Perhaps some parallels to the world of radiological findings here? How much harm and distress has been caused by conflating changes on a MRI scan with pain?
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Here here Tim 👏👏👏 but we have to be careful here! I believe I’m accurate in saying it takes over forty facial muscles to express physical and / or emotional pain but only less than twenty to express pleasure. If the neurotag has defaulted to a negative the recognition program is doomed …….