Northwestern University researchers have developed a new artificial intelligence (A.I.) platform that detects COVID-19 by analyzing X-ray images of the lungs.
Called DeepCOVID-XR, the machine-learning algorithm outperformed a team of specialized thoracic radiologists—spotting COVID-19 in X-rays about 10 times faster and 1-6% more accurately.
The researchers believe physicians could use the A.I. system to rapidly screen patients who are admitted into hospitals for reasons other than COVID-19. Faster, earlier detection of the highly contagious virus could potentially protect health care workers and other patients by triggering the positive patient to isolate sooner.
The study’s authors also believe the algorithm could potentially flag patients for isolation and testing who are not otherwise under investigation for COVID-19.
The study will be published on Nov. 24 in the journal Radiology.
“We are not aiming to replace actual testing,” said Northwestern’s Aggelos Katsaggelos, an A.I. expert and senior author of the study. “X-rays are routine, safe and inexpensive. It would take seconds for our system to screen a patient and determine if that patient needs to be isolated.”
“It could take hours or days to receive results from a COVID-19 test,” said Dr. Ramsey Wehbe, a cardiologist and postdoctoral fellow in A.I. at the Northwestern Medicine Bluhm Cardiovascular Institute. “A.I. doesn’t confirm whether or not someone has the virus. But if we can flag a patient with this algorithm, we could speed up triage before the test results come back.”
Katsaggelos is the Joseph Cummings Professor of Electrical and