New algorithms show accuracy, reliability in gauging unconsciousness under general anesthesia

Anesthestic drugs act on the brain, but most anesthesiologists rely on heart rate, respiratory rate, and movement to infer whether surgery patients remain unconscious to the desired degree. In a new study, a research team based at MIT and Massachusetts General Hospital shows that a straightforward artificial intelligence approach, attuned to the kind of anesthetic being used, can yield algorithms that assess unconsciousness in patients based on brain activity with high accuracy and reliability.

“One of the things that is foremost in the minds of anesthesiologists is ‘Do I have somebody who is lying in front of me who may be conscious and I don’t realize it?’ Being able to reliably maintain unconsciousness in a patient during surgery is fundamental to what we do,” says senior author Emery N. Brown, the Edward Hood Taplin Professor in The Picower Institute for Learning and Memory and the Institute for Medical Engineering and Science at MIT, and an anesthesiologist at MGH. “This is an important step forward.”

More than providing a good readout of unconsciousness, Brown adds, the new algorithms offer the potential to allow anesthesiologists to maintain it at the desired level while using less drug than they might administer when depending on less direct, accurate, and reliable indicators. That can improve patient’s post-operative outcomes, such as delirium.

“We may always have to be a little bit ‘overboard,’” says Brown, who is also a professor at Harvard Medical School. “But can we do it with sufficient accuracy so that we are not dosing people more than is needed?”

Used to drive an infusion pump, for instance, algorithms could help anesthesiologists precisely throttle drug delivery to optimize a patient’s state and the doses they are receiving.

Artificial intelligence, real-world testing

To develop the technology to do so, postdocs John Abel and Marcus Badgeley led the study, published in PLOS ONE, in which they trained machine learning algorithms on a remarkable dataset the lab gathered back in 2013. In that study, 10 healthy volunteers in their 20s underwent anesthesia with the commonly used drug propofol. As the dose was methodically raised using computer-controlled delivery, the volunteers were asked to respond to a simple request until they couldn’t anymore. Then when they were brought back to consciousness as the dose was later lessened, they became able to respond again. All the while, neural rhythms reflecting their brain activity were recorded with electroencephalogram (EEG) electrodes, providing a direct, real-time link between measured brain activity and exhibited unconsciousness.

In the new work, Abel, Badgeley, and the team trained versions of their AI algorithms, based on different underlying statistical methods, on more than 33,00

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Source - Continue Reading: https://news.mit.edu/2021/new-algorithms-show-accuracy-reliability-gauging-unconsciousness-under-general-anesthesia-0526

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