Samsung Medison and Intel Help Improve Anesthesia Delivery

Samsung Medison and Intel are collaborating on NerveTrack™, a real-time nerve tracking ultrasound feature that helps anesthesiologists identify nerves in a patient’s arm to help administer anesthesia quickly and accurately. Leveraging the Intel® Distribution of OpenVINO™ toolkit, Samsung Medison’s NerveTrack™ can potentially reduce scanning time by up to 30 percent, according to the company.


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Image credit: Samsung Medison

Ultrasound-guided regional anesthesia (UGRA) is becoming standard practice for needle-based interventions, including vascular access and peripheral nerve block. However, even with UGRA, it can be difficult for anesthesiologists to correctly identify nerves, which can be as small as 2 millimeters in diameter, or to see the needle tip properly. NerveTrack can automatically identify nerves in real time for anesthesiologists, reducing the possibility of complications while improving workflows.

“To keep up with the changing world of healthcare, you need trusted partners and flexible technologies. That’s why we teamed up with Intel to create our NerveTrack solution. With our combined industry expertise and cutting-edge solutions, we’re using innovative technologies to help practitioners identify nerves faster and more accurately. The result is potentially less risk, better patient outcomes and more efficient workflows,” says Dr. Won-Chul Bang, vice president for product strategy at Samsung Medison. NerveTrack can also be helpful for use in teaching hospitals for medical students and residents. 

Improving Treatment Workflows

NerveTrack was developed based on the Intel® Distribution of OpenVINO™ toolkit. It uses inference to detect and identify the location of a nerve area in real time during an ultrasound scan, improving the treatment workflow for anesthesiologists. 

To train Samsung’s proprietary real-time algorithm that automatically detects nerves in ultrasound images, a significant amount of clinical ultrasound data was required. And with Intel’s OpenVINO CVAT (Computer Vision Annotation Tool), the total volume of training data increased up to 7 times, leading to improved accuracy of more than 20 percent.

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