Sitting on a stool several meters from a long-armed robot,
Dr Danyal Fer wrapped his fingers around two metal handles near his chest.
As he moved the handles — up and down, left and right — the
robot mimicked each small motion with its own two arms. Then, when he pinched
his thumb and forefinger together, one of the robot’s tiny claws did much the
same. This is how surgeons like Fer have long used robots when operating on
patients. They can remove a prostate from a patient while sitting at a computer
console across the room.
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But after this brief demonstration, Fer and his fellow
researchers at the University of California, Berkeley, showed how they hope to
advance the state of the art. Fer let go of the handles, and a new kind of
computer software took over. As he and the other researchers looked on, the
robot started to move entirely on its own.
With one claw, the machine lifted a tiny plastic ring from
an equally tiny peg on the table, passed the ring from one claw to the other,
moved it across the table and gingerly hooked it onto a new peg. Then the robot
did the same with several more rings, completing the task as quickly as it had
when guided by Fer.
The training exercise was originally designed for humans;
moving the rings from peg to peg is how surgeons learn to operate robots like
the one in Berkeley. Now, an automated robot performing the test can match or
even exceed a human in dexterity, precision and speed, according to a new
research paper from the Berkeley team.
The project is a part of a much wider effort to bring
artificial intelligence into the operating room. Using many of the same
technologies that underpin self-driving cars, autonomous drones and warehouse
robots, researchers are working to automate surgical robots too. These methods
are still a long way from everyday use, but progress is accelerating.
“It is an exciting time,” said Russell Taylor, a professor
at Johns Hopkins University and former IBM researcher known in the academic
world as the father of robotic surgery. “It is where I hoped we would be 20
years ago.”
During a recent phone call, Greg Hager, a computer scientist
at Johns Hopkins, said that surgical automation would progress much like the
Autopilot software that was guiding his Tesla down the New Jersey Turnpike as
he spoke. The car was driving on its own, he said, but his wife still had her
hands on the wheel, should anything go wrong. And she would take over when it
was time to exit the highway.
“We can’t automate the whole process, at least not without
human oversight,” he said. “But we can start to build automation tools that
make the life of a surgeon a little bit easier.”
Five years ago, researchers with the Children’s National
Health System in Washington, DC, designed a robot that could automatically
suture the intestines of a pig during surgery. It was a notable step toward the
kind of future envisioned by Hager. But it came with an asterisk: The
researchers had implanted tiny markers in the pig’s intestines that emitted a
near-infrared light and helped guide the robot’s movements.
The method is far from practical, as the markers are not
easily implanted or removed. But in recent years, artificial intelligence
researchers have significantly improved the power of computer vision, which
could allow robots to perform surgical tasks on their own, without such
markers.
The change is driven by what are called neural networks,
mathematical systems that can learn skills by analyzing vast amounts of data.
By analyzing thousands of cat photos, for instance, a neural network can learn
to recognize a cat. In much the same way, a neural network can learn from
images captured by surgical robots.
Surgical robots are equipped with cameras that record
three-dimensional video of each operation. The video streams into a viewfinder
that surgeons peer into while guiding the operation, watching from the robot’s
point of view.
But afterward, these images also provide a detailed road map
showing how surgeries are performed. They can help new surgeons understand how
to use these robots, and they can help train robots to handle tasks on their
own. By analyzing images that show how a surgeon guides the robot, a neural
network can learn the same skills.
This is how the Berkeley researchers have been working to
automate their robot, which is based on the da Vinci Surgical System, a
two-armed machine that helps surgeons perform more than 1 million procedures a
year. Fer and his colleagues collect images of the robot moving the plastic
rings while under human control. Then their system learns from these images,
pinpointing the best ways of grabbing the rings, passing them between claws and
moving them to new pegs.
But this process came with its own asterisk. When the system
told the robot where to move, the robot often missed the spot by millimeters.
Over months and years of use, the many metal cables inside the robot’s twin
arms have stretched and bent in small ways, so its movements were not as
precise as they needed to be.
Human operators could compensate for this shift,
unconsciously. But the automated system could not. This is often the problem
with automated technology: It struggles to deal with change and uncertainty.
Autonomous vehicles are still far from widespread use because they aren’t yet
nimble enough to handle all the chaos of the everyday world.
The Berkeley team decided to build a new neural network that
analyzed the robot’s mistakes and learned how much precision it was losing with
each passing day. “It learns how the robot’s joints evolve over time,” said
Brijen Thananjeyan, a doctoral student on the team. Once the automated system
could account for this change, the robot could grab and move the plastics
rings, matching the performance of human operators.
Other labs are trying different approaches.
Axel Krieger, a
Johns Hopkins researcher who was part of the pig-suturing project in 2016, is
working to automate a new kind of robotic arm, one with fewer moving parts and
that behaves more consistently than the kind of robot used by the Berkeley
team.
Researchers at the
Worcester Polytechnic Institute are developing ways
for machines to carefully guide surgeons’ hands as they perform particular
tasks, like inserting a needle for a cancer biopsy or burning into the brain to
remove a tumor.
“It is like a car where the lane-following is autonomous but
you still control the gas and the brake,” said Greg Fischer, one of the
Worcester researchers.
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