CV19-NET surpassed three experienced thoracic radiologists in distinguishing pneumonia associated with COVID-19 from other types, achieving an area under the curve of 0.92 on the test dataset of 5,869 CXRs from the 2,193 patients acquired from multiple hospitals and multiple vendors. When presented with a set of 500 randomly selected test CXRs, the AI algorithm achieved an AUC of 0.94, compared to an AUC of 0.85 from the three.

Many radiologists are only just seeing COVID-19-induced pneumonia cases for the first time, and require more cases to read in order to learn the common and unique imaging features associated with the disease. This, along with similar algorithms in development, is expected to help bridge this learning gap.

"Since the start of the pandemic, many AI researchers, including medical physicists, computer scientists, biomedical engineers, and healthcare industry researchers have been working on AI algorithms like this," Guang-Hong Chen, Ph.D., professor of medical physics and radiology at University of Wisconsin-Madison, told HCB News. "In the next year or so, there will be more algorithms available to differentiate symptoms of COVID-19 from non-COVID-19 conditions. The deployment of the algorithms like this may help healthcare providers quickly identify those patients with high risk of COVID-19 from other patients without COVID-19, despite similar clinical symptoms such as fever or cough."

The findings were published in Radiology.

Back to HCB News

X-Ray Homepage