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.