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AI Can Detect COVID-19 in Lungs as Well as Virtual physician

Stephens, Keri. AXIS Imaging News; Overland Park (Sep 30, 2020).

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A University of Central Florida researcher is part of a new study showing that artificial intelligence (AI) can be nearly as accurate as a physician in diagnosing COVID-19 in the lungs. The study, recently published in Nature Communications , shows the new technique can also overcome some of the challenges of current testing.

Researchers demonstrated that an AI algorithm could be trained to classify COVID-19 pneumonia in CT scans with up to 90% accuracy, as well as correctly identify positive cases 84% of the time and negative cases 93% of the time. CT scans offer a deeper insight into COVID-19 diagnosis and progression as compared to the often-used reverse transcription-polymerase chain reaction, or RT-PCR, tests. These tests have high false negative rates, delays in processing and other challenges.

Another benefit to CT scans is that they can detect COVID-19 in people without symptoms, in those who have early symptoms, during the height of the disease and after symptoms resolve. However, CT is not always recommended as a diagnostic tool for COVID-19 because the disease often looks similar to influenza-associated pneumonias on the scans.

The new UCF co-developed algorithm can overcome this problem by accurately identifying COVID-19 cases, as well as distinguishing them from influenza, thus serving as a great potential aid for physicians, says Ulas Bagci, an assistant professor in UCF’s Department of Computer Science. Bagci was a co-author of the study and helped lead the research.

“We demonstrated that a deep learning-based AI approach can serve as a standardized and objective tool to assist healthcare systems as well as patients,” Bagci says. “It can be used as a complementary test tool in very specific limited populations, and it can be used rapidly and at large scale in the unfortunate event of a recurrent outbreak.”

Bagci is an expert in developing...