New research shows that artificial intelligence (AI) can identify COVID-19 in lung ultrasound images, similar to facial recognition software spotting faces in crowds. Published in Communications Medicine, these findings enhance medical diagnostics, allowing for swift detection of COVID-19 and other lung diseases. This effort originated from the necessity to rapidly assess patients in overwhelmed emergency rooms during the early pandemic stages.

“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,” says senior author Muyinatu Bell, PhD, an associate professor of electrical and computer engineering, biomedical engineering, and computer science at Johns Hopkins University in Baltimore. “Potentially, we want to have wireless devices that patients can use at home to monitor progression of COVID-19, too.”

The tool also holds potential for developing wearables that track such illnesses as congestive heart failure, which can lead to fluid overload in patients’ lungs, not unlike COVID-19, says co-author Tiffany Fong, MD, an assistant professor of emergency medicine at Johns Hopkins Medicine.

“What we are doing here with AI tools is the next big frontier for point of care,” Fong says. “An ideal use case would be wearable ultrasound patches that monitor fluid buildup and let patients know when they need a medication adjustment or when they need to see a doctor.”

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.

“We had to model the physics of ultrasound and acoustic wave propagation well enough in order to get believable simulated images,” Bell says. “Then we had to take it a step further to train our computer models to use these simulated data to reliably interpret real scans from patients with affected lungs.”

Early in the pandemic, scientists struggled to use artificial intelligence to assess COVID-19 indicators in lung ultrasound images because of a lack of patient data and because they were only beginning to understand how the disease manifests in the body, Bell said.

Her 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.

“Early in the pandemic, we didn’t have enough ultrasound images of COVID-19 patients to develop and test our algorithms, and as a result our deep neural networks never reached peak performance,” adds first author Lingyi Zhao, PhD, who developed the software while a postdoctoral fellow in Bell’s lab and is now working at Novateur Research Solutions. “Now, we are proving that with computer-generated datasets we still can achieve a high degree of accuracy in evaluating and detecting these COVID-19 features.”