Researchers at Rutgers University have developed a robot that utilizes artificial intelligence, along with near-infrared and ultrasound imaging, to automatically make blood draws and insert catheters in small blood vessels. The device could take the guesswork out of blood draws, which are frequently challenging when performed manually, and reduce the chances of complications, while allowing medical staff to focus on other tasks.
Accessing blood vessels can be challenging in a wide variety of patients, including pediatric, elderly, and those that are chronically ill. Such patients are more likely to have twisted, small, or rolled vessels, making needle or catheter placement very difficult. The chances of successfully inserting a needle on the first attempt are less than 50% in such patients, and frequently upwards of five attempts are required.
As healthcare staff can find it difficult to see the vein they are attempting to access, complications can arise when other structures are damaged by a needle insertion, such as nearby arteries, organs, or nerves. To address these issues, the Rutgers researchers created a robotic system that can automatically find blood vessels, even if they are very small, and successfully insert a needle or catheter.
The robot is small enough to sit on a benchtop and uses ultrasound and near-infrared imaging to image the tissue, and artificial intelligence to interpret the images and detect blood vessels and calculate their depth and size.
“Using volunteers, models and animals, our team showed that the device can accurately pinpoint blood vessels, improving success rates and procedure times compared with expert health care professionals, especially with difficult to access blood vessels,” said Martin Yarmush, a researcher involved in the study, in a Rutgers press release. “Not only can the device be used for patients, but it can also be modified to draw blood in rodents, a procedure which is extremely important for drug testing in animals in the pharmaceutical and biotech industries.”
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Study in Nature Machine Intelligence: Deep learning robotic guidance for autonomous vascular access
Via: Rutgers University