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Providing surgical robots with a new type of machine intelligence to make them easier and more intuitive for surgeons to operate is the goal of a major new five-year grant from the National Science Foundation given to a collaboration of research teams at Vanderbilt University, Nashville, TN; Carnegie Mellon University, Pittsburgh, PA; and Johns Hopkins University, Baltimore, MD.

One of the project’s objectives is to restore the type of sensory awareness surgeons have during open surgery, where they can directly see and touch internal organs and tissue, which is generally lost through minimally invasive surgery working through small incisions in a patient’s skin.

Surgeons have attempted to compensate for the loss of direct sensory feedback through pre-operative imaging, where they use techniques like MRI, X-ray imaging, and ultrasound to map the internal structure of the body before they operate. They have employed miniaturized lights and cameras to provide them with visual images of the tissue immediately in front of surgical probes. They have also developed methods that track the position of the probe as they operate and plot its position on pre-operative maps.

The researchers intend to create a system that acquires data from a number of different types of sensors as an operation is underway and integrates them with pre-operative information to produce dynamic, real-time maps that precisely track the position of the robot probe and show how the tissue in its vicinity responds to its movements.

For example, adding pressure sensors to robot probes will provide real time information on how much force the probe is exerting against the tissue surrounding it. Not only does this make it easier to work without injuring the tissue but it can also be used to “palpate” tissue to search for hidden tumor edges, arteries, and aneurisms. Such sensor data can also feed into computer simulations that predict how various body parts shift in response to the probe’s movements.

The engineers also intend to create what they call “virtual fixtures,” which are pre-programmed restrictions on the robot’s actions. For example, a robot might be instructed not to cut in an area where a major blood vessel has been identified. Not only would this prevent the robot from cutting a blood vessel when operating autonomously, but it would also prevent a surgeon from doing so accidently when operating the robot manually. The teams will be using several different experimental robots during this research.

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