Model used to prioritize cases and accelerate time-to-care
MINNEAPOLIS, Minn., June 20, 2020 – vRad (Virtual Radiologic), announced that Robert J. Harris, Ph.D., vRad Data Scientist / Machine Learning Engineer, will make a scientific presentation, “Classification of Endotracheal Tube Positioning on Chest XR using a Convolutional Neural Net Trained with Annotated Images,” at the Society for Imaging Informatics in Medicine (SIIM) Annual Meeting on June 25. The full session details can be found here.
The presentation highlights how vRad, using machine learning on retrospective data, is creating an artificial intelligence (AI) model for classifying and prioritizing malpositioned ET tubes on Chest X-Rays that pass through vRad’s Patented Reading Platform.
Endotracheal tube intubation is often used when patients are ill and require respiratory assistance. These tubes must be positioned properly in relation to the carina; too high and the lungs may not be respirated, too low and only one lung may be respirated. Our institution receives approximately 4,000 XR Chest images every day, 5% of which contain an endotracheal tube. If the tube is determined to be malpositioned by the reading radiologist, this information is relayed back to the site for tube adjustment.
vRad (Virtual Radiologic) is the nation’s leading cloud-based radiology solutions and telemedicine company with approximately 500 U.S. board-certified and subspecialty trained physicians. Its clinical expertise and evidence-based insight help clients make better decisions about the treatment and health of their patients, as well as their imaging services. vRad interprets and processes patient imaging studies on the world’s largest and most advanced cloud-based radiology PACS, the vRad Imaging Platform, for more than 2,000 facilities and radiology groups across all 50 states, the District of Columbia, and Puerto Rico. The practice has 19 issued patents for innovation in telemedicine workflow and is a recognized leader in imaging analytics and deep learning-assisted diagnostics. For more information, visit www.www.vrad.com.