A new study by researchers at MIT and Massachusetts General Hospital suggests the day may be approaching when advanced artificial intelligence systems could assist anesthesiologists in the operating room, according to a release.
In a special edition of Artificial Intelligence in Medicine, the team of neuroscientists, engineers and physicians demonstrated a machine learning algorithm for continuously automating dosing of the anesthetic drug propofol.
Using an application of deep reinforcement learning, in which the software’s neural networks simultaneously learned how its dosing choices maintain unconsciousness and how to critique the efficacy of its own actions, the algorithm outperformed more traditional software in sophisticated, physiology-based simulations of patients. It also closely matched the performance of real anesthesiologists when showing what it would do to maintain unconsciousness given recorded data from nine real surgeries.
The algorithm’s advances increase the feasibility for computers to maintain patient unconsciousness with no more drug than is needed, thereby freeing up anesthesiologists for all the other responsibilities they have in the operating room, including making sure patients remain immobile, experience no pain, remain physiologically stable, and receive adequate oxygen said co-lead authors Gabe Schamberg and Marcus Badgeley.
Senior author Emery N. Brown, a neuroscientist at The Picower Institute for Learning and Memory and Institute for Medical Engineering and Science at MIT and an anesthesiologist at MGH, said the algorithm’s potential to help optimize drug dosing could improve patient care.
The research team designed a machine learning approach that would not only learn how to dose propofol to maintain patient unconsciousness, but also how to do so in a way that would optimize the amount of drug administered. They accomplished this by endowing the software with two related neural networks: an “actor” with the responsibility to decide how much drug to dose at every given moment, and a “critic” whose job was to help the actor behave in a manner that maximizes “rewards” specified by the programmer. For instance, the researchers experimented with training the algorithm using three different rewards: one that penalized only overdosing, one that questioned providing any dose, and one that imposed no penalties.
In every case they trained the algorithm with simulations of patients that employed advanced models of both pharmacokinetics, or how quickly propofol doses reach the relevant regions of the brain after doses are administered, and pharmacodynamics, or how the drug actually alters consciousness when it reaches its destination. Patient unconsciousness levels, meanwhile, were reflected in measure of brain waves as they can be in real operating rooms. By running hundreds of rounds of simulation with a range of values for these conditions, both the actor and the critic could learn how to perform their roles for a variety of kinds of patients.