Researchers develop AI to detect Covid on chest x-rays

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 more accurate.

According to the study, published in the journal Radiology, the research team believe physicians could use the AI system to rapidly screen patients who are admitted to 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.

"We are not aiming to replace actual testing. 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," said study author Aggelos Katsaggelos from Northwestern  University in the US.

To develop, train and test the new algorithm, the researchers used 17,002 chest X-ray images -- the largest published clinical dataset of chest X-rays from the Covid-19 era used to train an AI system.

Of those images, 5,445 came from Covid-19-positive patients from sites across the Northwestern Memorial Healthcare System.

The team then tested DeepCOVID-XR against five experienced cardiothoracic fellowship-trained radiologists on 300 random test images from Lake Forest Hospital.

Each radiologist took approximately two-and-a-half to three-and-a-half hours to examine this set of images, whereas the AI system took about 18 minutes.

The radiologists' accuracy ranged from 76-81 per cent. DeepCOVID-XR performed slightly better at 82 per cent accuracy.

"Radiologists are expensive and not always available," Katsaggelos said.

"X-rays are inexpensive and already a common element of routine care. This could potentially save money and time -- especially because timing is so critical when working with Covid-19," the author noted.