Also, it correctly 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.
“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 added.
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.
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.
“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,” Bagci said.