In a new study, researchers have developed a system that measures a patient’s pain level by analyzing brain activity from a portable neuroimaging device.
The new device could help doctors diagnose and treat pain in unconscious and noncommunicative patients and reduce the risk of chronic pain that can occur after surgery.
The research was conducted by a team from MIT and other institutes.
Pain management is a big challenging, complex balancing act.
Over-treating pain runs the risk of addicting patients to pain medication, but under-treating pain may lead to long-term chronic pain and other complications.
Currently, doctors generally gauge pain levels according to their patients’ own reports of how they’re feeling.
But this method is not useful for people who can’t communicate how they’re feeling effectively, such as children, elderly patients with dementia, or those undergoing surgery.
In the new study, the team developed a method to quantify pain in patients.
They used an emerging neuroimaging technique called functional near-infrared spectroscopy (fNIRS), in which sensors placed around the head measure oxygenated hemoglobin concentrations that indicate neuron activity.
The team used only a few fNIRS sensors on a patient’s forehead to measure activity in the prefrontal cortex, which plays a major role in pain processing.
Using the measured brain signals, they developed personalized machine-learning models to detect patterns of oxygenated hemoglobin levels linked to pain responses.
They found the models can detect whether a patient is experiencing pain with around 87% accuracy.
The team says they have a solid basis for bringing this technology to a real-world clinical setting.
They hope this new device could help improve pain detection in more patients.
One author of the study is Daniel Lopez-Martinez, a Ph.D. student in the Harvard-MIT Program.
The study was presented at the International Conference on Affective Computing and Intelligent Interaction.
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