<p>Researchers have shown that an artificial intelligence (AI) algorithm could be trained to classify COVID-19 pneumonia in X-ray scans with up to 90 per cent accuracy, an advance that can lead to the development of a complementary test tool, particularly for vulnerable populations.</p>.<p>The study, published in the journal Nature Communications, demonstrated that the new technique can correctly identify positive COVID-19 cases 84 per cent of the time, and negative cases 93 per cent of the time.</p>.<p>According to the scientists, including those from the University of Central Florida (UCF) in the US, computed tomography (CT) X-ray scans can offer a deep insight into COVID-19 diagnosis and progression.</p>.<p>They said conventionally used reverse transcription-polymerase chain reaction, or RT-PCR tests, have high false negative rates, and have other challenges like delays in processing.</p>.<p>With CT scans, they said, clinicians can detect COVID-19 in people without symptoms, and also in those who have early symptoms, during the height of the disease, and after symptoms resolve.</p>.<p>However, the scientists said the X ray scan is not always recommended as a diagnostic tool for COVID-19 since the disease often looks similar to influenza-associated pneumonias on the scans.</p>.<p>In the new study, the researchers showed that their algorithm can overcome this problem by accurately identifying COVID-19 cases, as well as distinguishing them from influenza.</p>.<p>They believe the algorithm can serve as a great potential aid for physicians.</p>.<p>"We demonstrated that a deep learning-based AI approach can serve as a standardised and objective tool to assist healthcare systems as well as patients," said study co-author Ulas Bagci from UCF.</p>.<p>"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 said.</p>.<p>In the study, the researchers trained a computer algorithm to recognise COVID-19 in lung CT scans of 1,280 multinational patients from China, Japan and Italy.</p>.<p>They then tested the algorithm on CT scans of 1,337 patients with lung diseases ranging from COVID-19 to cancer and non-COVID pneumonia.</p>.<p>When the scientists 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.</p>.<p>"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 pneumonias, and demonstrate sufficient generalisability to unseen patient populations and centers," Bagci said.</p>
<p>Researchers have shown that an artificial intelligence (AI) algorithm could be trained to classify COVID-19 pneumonia in X-ray scans with up to 90 per cent accuracy, an advance that can lead to the development of a complementary test tool, particularly for vulnerable populations.</p>.<p>The study, published in the journal Nature Communications, demonstrated that the new technique can correctly identify positive COVID-19 cases 84 per cent of the time, and negative cases 93 per cent of the time.</p>.<p>According to the scientists, including those from the University of Central Florida (UCF) in the US, computed tomography (CT) X-ray scans can offer a deep insight into COVID-19 diagnosis and progression.</p>.<p>They said conventionally used reverse transcription-polymerase chain reaction, or RT-PCR tests, have high false negative rates, and have other challenges like delays in processing.</p>.<p>With CT scans, they said, clinicians can detect COVID-19 in people without symptoms, and also in those who have early symptoms, during the height of the disease, and after symptoms resolve.</p>.<p>However, the scientists said the X ray scan is not always recommended as a diagnostic tool for COVID-19 since the disease often looks similar to influenza-associated pneumonias on the scans.</p>.<p>In the new study, the researchers showed that their algorithm can overcome this problem by accurately identifying COVID-19 cases, as well as distinguishing them from influenza.</p>.<p>They believe the algorithm can serve as a great potential aid for physicians.</p>.<p>"We demonstrated that a deep learning-based AI approach can serve as a standardised and objective tool to assist healthcare systems as well as patients," said study co-author Ulas Bagci from UCF.</p>.<p>"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 said.</p>.<p>In the study, the researchers trained a computer algorithm to recognise COVID-19 in lung CT scans of 1,280 multinational patients from China, Japan and Italy.</p>.<p>They then tested the algorithm on CT scans of 1,337 patients with lung diseases ranging from COVID-19 to cancer and non-COVID pneumonia.</p>.<p>When the scientists 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.</p>.<p>"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 pneumonias, and demonstrate sufficient generalisability to unseen patient populations and centers," Bagci said.</p>