Radiologists use deep learning to find signs of COVID-19 in chest X-rays
Johns Hopkins radiologists have found that a deep learning algorithm to detect tuberculosis in chest X-rays could be useful for identifying lung abnormalities related to COVID-19. These findings, published online in the Journal of Thoracic Imaging, suggest that deep learning systems could potentially assist clinicians in triaging and treating these high-risk patients, as well as help overcome the scarcity of COVID-19 images available for machine learning development.
The study was based on the observation that chest X-ray abnormalities from COVID-19 appear very similar to those of TB patients. Chest X-rays have been proposed as a potentially useful tool for assessing COVID-19 patients, especially in overwhelmed emergency departments and urgent care centers, but the research team hypothesized that a deep learning model already trained to identify TB in X-rays would also work well to identify signs of the novel coronavirus.
“We found good generalization of our TB model toward COVID-19,” says radiology resident Paul Yi, co-director of the Radiology AI Lab and affiliate faculty of the Malone Center for Engineering in Healthcare. “Our goal was to demonstrate the ability of a deep learning model that had never ‘seen’ a case of COVID-19 to identify these cases. Because COVID-19 is a new infection, large datasets are not currently available to train deep learning models. We hypothesized that images of other infections with similar appearances to COVID-19 could be used to train models capable of identifying this new disease.”