Coronavirus Disease 2019 (COVID-19) has been a global pandemic with an exponential growth rate and an incompletely understood transmission process. Researchers have demonstrated that an artificial intelligence (AI) algorithm could be trained to classify Covid-19 pneumonia in computed tomography (CT) scans with up to 90 per cent accuracy.
It also identifies positive cases 84 per cent of the time and negative cases 93 per cent of the time. The study, recently published in Nature Communications, shows the new technique can also overcome some of the challenges of current testing.
“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,” said study author Ulas Bagci from the University of Central Florida in the US.
According to the researchers, 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 pneumonia on the scans.
The new 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, the researchers said.
“We showed that robust AI models can achieve up to 90 per cent accuracy in independent test populations, maintain high specificity in non-Covid-19 related pneumonia, and demonstrate sufficient generalizability to unseen patient populations and centres,” they said.
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