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New AI Platform Detects COVID-19 on Chest X-Rays with Accuracy and Speed

By HospiMedica International staff writers
Posted on 25 Nov 2020
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Image: Generated heatmaps appropriately highlighted abnormalities in the lung fields in those images accurately labeled as COVID-19 positive (A-C) in contrast to images which were accurately labeled as negative for COVID-19 (D). Intensity of colors on the heatmap correspond to features of the image that are important for prediction of COVID-19 positivity (Photo courtesy of Northwestern University)
Image: Generated heatmaps appropriately highlighted abnormalities in the lung fields in those images accurately labeled as COVID-19 positive (A-C) in contrast to images which were accurately labeled as negative for COVID-19 (D). Intensity of colors on the heatmap correspond to features of the image that are important for prediction of COVID-19 positivity (Photo courtesy of Northwestern University)
A new artificial intelligence (AI) platform that detects COVID-19 by analyzing X-ray images of the lungs is about 10 times faster as well as 1-6% more accurate than individual specialized radiologists.

Called DeepCOVID-XR, the machine-learning algorithm developed by researchers at the Northwestern University (Evanston, IL, USA) 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 AI 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 researchers also believe the algorithm could potentially flag patients for isolation and testing who are not otherwise under investigation for COVID-19.

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. The team then tested DeepCOVID-XR against five experienced cardiothoracic fellowship-trained radiologists on 300 random test images. 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%. DeepCOVID-XR performed slightly better at 82% accuracy. The researchers have made the algorithm publicly available with hopes that others can continue to train it with new data. Right now, DeepCOVID-XR is still in the research phase, but could potentially be used in the clinical setting in the future.

"We are not aiming to replace actual testing," said Northwestern's Aggelos Katsaggelos, an AI 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 AI at the Northwestern Medicine Bluhm Cardiovascular Institute. "AI 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."

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