16 Oct UW researchers find more precise way to detect COVID-19 pneumonia
Using cutting-edge artificial intelligence technology, University of Wisconsin‒Madison investigators have developed a far more precise way to identify cases of COVID-19-induced pneumonia.
Using a custom artificial intelligence algorithm called CV19-Net, the UW research team dug into a vast resource database of tens of thousands of COVID-19 chest X-rays to show its method could identify pneumonia caused by COVID-19 at a sensitivity of 88 percent, according to Guang-Hong Chen, professor of medical physics and radiology at the UW School of Medicine and Public Health.
From the tens of thousands of X-rays available, the team pared down the number of X-ray images to train the algorithm and then evaluated its performance over 5,900 X-rays from approximately 3,000 patients between Feb. 1 and May 3.
To compare to diagnoses generated by the human eye, Chen’s team asked three expert thoracic radiologists experienced with COVID-19 pneumonia X-ray images to examine 500 chest X-ray images from the CV19-Net database. The three radiologists were able to correctly perform diagnosis with accuracy of 76 percent, 68 percent and 72 percent. In contrast, the CV19-Net algorithm examined the images and achieved a diagnostic accuracy of 84 percent.