The machine-learning algorithm has outperformed a team of specialized thoracic radiologists by spotting Covid-19 in X-rays about 10 times faster and 1-6 per cent more accurately.
To develop, train, and test the new algorithm, the researchers used 17,002 chest X-ray images. Of those images, 5,445 came from Covid-19-positive patients from sites across the Northwestern Memorial Healthcare System.
The researchers 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. DeepCovidD-XR performed slightly better at 82-per cent accuracy.
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“Radiologists are expensive and not always available,” said NU’s Aggelos Katsaggelos, an AI expert and senior author of the study. “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.”
NU researchers have made the algorithm publicly available with hopes that others can continue to train it with new data. DeepCovid-XR is still in the research phase, but could potentially be used in the clinical setting in the future.
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