Spurred by the COVID-19 pandemic, Princeton researchers have developed a diagnostic tool to analyze chest X-rays for patterns in diseased lungs. The new tool could give doctors valuable information about a patient’s condition, quickly and cheaply, at the point of care.
Jason Fleischer, professor of electrical engineering and the project’s principal investigator, said he was inspired to create the tool after reading about COVID-19’s devastating range of attacks. As hospitals have been overrun with patients, doctors have observed two basic types of lung damage, one more immediately life-threatening than the other. Treatment can differ between the types, so distinguishing the two could improve care and better allocate scarce resources.
While current differentiation methods involve expensive and time-consuming procedures, such as computed tomography (CT) scans, Fleischer’s machine learning model looks at a simple X-ray image and finds patterns that are too subtle even for the expert human eye. This tool would give doctors a new measure for determining the type and severity of COVID-19 pneumonia. And the process, on the ground, is simple.
“Importantly, there is no change in practice,” Fleischer said. “The technician doesn’t have to do anything differently. Hospitals don’t have to do any new procedure. With the X-rays they already have—and routinely take—we can give them this extra information.”
Fleischer and graduate student Mohammad Tariqul Islam posted a paper detailing their work on medrxiv (pronounced med archive), a server for scientists to share results in the form of early drafts while a paper undergoes the formal editorial process. At the time of this writing, Fleischer’s paper “Distinguishing L and H phenotypes of COVID-19 using a