According to a March 20 news article from Johns Hopkins University, artificial intelligence (AI) can now identify COVID-19 in lung ultrasound images, new research shows.
The findings have recently been published in Communications Medicine. The effort, according to the article, started early in the pandemic when clinicians were seeking out tools to quickly assess legions of patients in overwhelmed emergency rooms.
Senior author Muyinatu Bell, an associate professor in the Department of Electrical and Computer Engineering in the Whiting School of Engineering at Johns Hopkins University was quoted in the article saying that, "We developed this automated detection tool to help doctors in emergency settings with high caseloads of patients who need to be diagnosed quickly and accurately, such as in the earlier stages of the pandemic. Potentially, we want to have wireless devices that patients can use at home to monitor progression of COVID-19, too."
The tool, according to co-author Tiffany Fong, an assistant professor of emergency medicine at Johns Hopkins Medicine, can potentially be developed for wearables to track other illnesses.
The release states, “The AI analyzes ultrasound lung images to spot features known as B-lines, which appear as bright, vertical abnormalities and indicate inflammation in patients with pulmonary complications. It combines computer-generated images with real ultrasounds of patients — including some who sought care at Johns Hopkins.”
Further, “Her [Bell’s] team developed software that can learn from a mix of real and simulated data and then discern abnormalities in ultrasound scans that indicate a person has contracted COVID-19. The tool is a deep neural network, a type of AI designed to behave like the interconnected neurons that enable the brain to recognize patterns, understand speech, and achieve other complex tasks.
Johns Hopkins University has the article.
