Research advances technology of AI assistance for anesthesiologists


Date:
February 2, 2022
Source:
Picower Institute at MIT
Summary:
A new deep learning algorithm trained to optimize doses of propofol
to maintain unconsciousness during general anesthesia could aid
anesthesiologists and augment monitoring, according to a new study.



FULL STORY ==========================================================================
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.


==========================================================================
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.

"One can think of our goal as being analogous to an airplane's auto-pilot
where the captain is always in the cockpit paying attention," said
Schamberg, a former MIT postdoc who is also the study's corresponding
author.

"Anesthesiologists have to simultaneously monitor numerous aspects of a patient's physiological state, and so it makes sense to automate those
aspects of patient care that we understand well." 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.

"Algorithms such as this one allow anesthesiologists to maintain more
careful, near continuous vigilance over the patient during general
anesthesia," said Brown, Edward Hood Taplin Professor Computational Neuroscience and Health Sciences & Technology at MIT.



==========================================================================
Both actor and critic 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.

The most effective reward system turned out to be the "dose penalty"
one in which the critic questioned every dose the actor gave, constantly chiding the actor to keep dosing to a necessary minimum to maintain unconsciousness.

Without any dosing penalty the system sometimes dosed too much and with
only an overdose penalty it sometimes gave too little. The "dose penalty"
model learned more quickly and produced less error than the other value
models and the traditional standard software, a "proportional integral derivative" controller.

An able advisor After training and testing the algorithm with simulations, Schamberg and Badgeley put the "dose penalty" version to a more real-world
test by feeding it patient consciousness data recorded from real cases
in the operating room. The testing demonstrated both the strengths and
limits of the algorithm.



========================================================================== During most tests the algorithm's dosing choices closely matched those of
the attending anesthesiologists after unconsciousness had been induced
and before it was no longer necessary. The algorithm, however, adjusted
dosing as frequently as every five seconds while the anesthesiologists
(who all had plenty of other things to do) typically did so only every
20-30 minutes, Badgeley noted.

As the tests showed, the algorithm is not optimized for inducing unconsciousness in the first place, the researchers acknowledged. The
software also doesn't know of its own accord when surgery is over,
they added, but it's a straightforward matter for the anesthesiologist
to manage that process.

One of the most important challenges any AI system is likely to continue
to face, Schamberg said, is whether the data it is being fed about patient unconsciousness is perfectly accurate. Another active area of research
in the Brown lab at MIT and MGH is in improving the interpretation of
data sources, such as brain wave signals, to improve the quality of
patient monitoring data under anesthesia.

In addition to Schamberg, Badgeley and Brown, the paper's other authors
are Benyamin Meschede-Krasa and Ohyoon Kwon.

The JPB Foundation and the National Insititutes of Health funded the
study.

========================================================================== Story Source: Materials provided by Picower_Institute_at_MIT. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Gabriel Schamberg, Marcus Badgeley, Benyamin Meschede-Krasa,
Ohyoon Kwon,
Emery N. Brown. Continuous action deep reinforcement learning for
propofol dosing during general anesthesia. Artificial Intelligence
in Medicine, 2022; 123: 102227 DOI: 10.1016/j.artmed.2021.102227 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220202091927.htm

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