In a new study, researchers found that artificial intelligence (AI) can be nearly as accurate as a physician in diagnosing COVID-19 in the lungs.
They found the new technique can also overcome some of the challenges of current testing.
The research was conducted by a team at the University of Central Florida
In the study, the team showed that an AI algorithm could be trained to classify COVID-19 pneumonia in computed tomography (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 pneumonia on the scans.
The new AI algorithm in this study 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.
To perform the study, the researchers trained a computer algorithm to recognize COVID-19 in lung CT scans of 1,280 multinational patients from China, Japan, and Italy.
Then they tested the algorithm on CT scans of 1,337 patients with lung diseases ranging from COVID-19 to cancer and non-COVID pneumonia.
When they compared the computer’s diagnoses with ones confirmed by physicians, they found that the algorithm was extremely proficient in accurately diagnosing COVID-19 pneumonia in the lungs and distinguishing it from other diseases, especially when examining CT scans in the early stages of disease progression.
The team says the AI can be used as a complementary test tool in very specific limited populations, and it can be used rapidly and at a large scale in the unfortunate event of a recurrent outbreak.
One author of the study is Ulas Bagci, an assistant professor in UCF’s Department of Computer Science.
The study is published in Nature Communications.
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