{"id":265,"date":"2013-04-17T14:20:27","date_gmt":"2013-04-17T21:20:27","guid":{"rendered":"http:\/\/www.stanford.edu\/group\/sailsbury_robotx\/cgi-bin\/salisbury_lab\/?page_id=265"},"modified":"2021-04-30T14:53:43","modified_gmt":"2021-04-30T21:53:43","slug":"haemobot-robotic-iv-catheter-insertio","status":"publish","type":"page","link":"https:\/\/sr.stanford.edu\/?page_id=265","title":{"rendered":"HaemoBot: Robotic IV Catheter Insertion"},"content":{"rendered":"\n\n\n
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Project Description<\/h2>\n

Nearly 250 million intravenous (IV) catheterizations take place in the United States annually, and\u00a028% of those insertions fail on the first attempt in normal adults. The failure rate for children\u00a0is as high as 54%. At least 27% of patients require 3 attempts or more to obtain a successful\u00a0insertion. Beyond producing pain and disfiguration, failed sticks can result in permanent\u00a0injury such as venous scarring and nerve damage. In patients with chronic illnesses that require\u00a0repeated insertions, the veins can scar to an extent that insertions are impossible in hands or arms.<\/p>\n

We aim to develop an assistive robotic system that will make IV catheterization more reliable and\u00a0less painful. Such a system would enhance the sensory and motor abilities of human practitioners\u00a0while keeping the human in full control of the insertion via a haptic device (high degree-of-freedom\u00a0joystick).<\/p>\n

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Practitioners often have great difficulty seeing or feeling small veins, as found in women and\u00a0children, or veins that lie beneath a layer of fat, as found in children and obese patients. One way\u00a0to visualize veins that are particularly difficult to detect is the use of infrared imaging. Although\u00a0several commercial devices use such imaging, they cannot interpret the images to find an\u00a0insertion point in the venous network. Towards this end, we are developing an algorithm that\u00a0searches infrared images of the hand for venous bifurcations as insertion sites. Such automatic\u00a0vein-finding could be used to suggest insertion points to a human practitioner, or for autonomous\u00a0insertion altogether. The above figures show bifurcations that were correctly identified by the algorithm,\u00a0with green arrows representing the IV needles.<\/p>\n

Our preliminary robot, as shown above, has seven degrees of freedom to give full motion control of the needle and catheter. It is designed with clinical convenience and safety in mind. We are investigating motions that the robot can perform that are too difficult for a human practitioner but that facilitate insertion. To provide a better understanding of the mechanics and limitations of IV insertion by human practitioners, we are currently conducting a clinical study with the Stanford Department of Anesthesiology. The study consists of measuring the forces, accelerations, and motions during IV insertions on 60 patients.<\/p>\n

Vein-Finding YouTube Video<\/h2>\n