Bioengineers have recently developed a self-reliant medical robot that can make its way into the blood-filled heart of a patient.
The new finding will help clinicians operate at a level of skill and experience equivalent to the best in their field, claim researchers who were involved in this study that was published in the Journal of Science Robotics
Surgeons have used robots operated by joysticks for more than a decade, and teams have shown that tiny robots can be steered through the body by external forces.
However, senior investigator Pierre Dupont said to his knowledge, this is the first report of the equivalent of a self-driving car navigating to a desired destination inside the body.
"The right way to think about this is through the analogy of a fighter pilot and a fighter plane," he said. "The fighter plane takes on the routine tasks like flying the plane, so the pilot can focus on the higher-level tasks of the mission."
The team's robotic catheter (medical robot) navigated using an optical touch sensor, informed by a map of the cardiac anatomy and preoperative scans.
The touch sensor uses artificial intelligence (AI) and image processing algorithms to enable the catheter to figure out where it is in the heart and where it needs to go.
For the demo, the team performed a highly technically demanding procedure known as paravalvular aortic leak closure, which repairs replacement heart valves that have begun leaking around the edges.
Once the robotic catheter reached the leak location, an experienced cardiac surgeon took control and inserted a plug to close the leak.
In repeated trials, the robotic catheter successfully navigated to heart valve leaks in roughly the same amount of time as the surgeon (using either a hand tool or a joystick-controlled robot).
Through a navigational technique called "wall following," the robotic catheter's optical touch sensor sampled its environment at regular intervals, in much the way insects' antennae or the whiskers of rodents sample their surroundings to build mental maps of unfamiliar, dark environments.
The sensor told the catheter whether it was touching blood, the heart wall or a valve (through images from a tip-mounted camera) and how hard it was pressing (to keep it from damaging the beating heart).
Data from preoperative imaging and machine learning algorithms helped the catheter interpret visual features.
In this way, the robotic catheter advanced by itself from the base of the heart, along the wall of the left ventricle and around the leaky valve until it reached the location of the leak.
"The algorithms help the catheter figure out what type of tissue it's touching, where it is in the heart, and how it should choose its next motion to get where we want it to go," Dupont explained.
Though the autonomous robot took a bit longer than the surgeon to reach the leaky valve, its wall-following technique meant that it took the longest path.
As the Food and Drug Administration begins to develop a regulatory framework for AI-enabled devices, Dupont envisions the possibility of autonomous surgical robots all over the world pooling their data to continuously improve performance over time -- much like self-driving vehicles in the field send their data back to Tesla to refine its algorithms.
"This would not only level the playing field, it would raise it. Every clinician in the world would be operating at a level of skill and experience equivalent to the best in their field. This has always been the promise of medical robots. Autonomy may be what gets us there," he mentioned.
