21 COVID-19 RESPONSE
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SUPPORTING ACCURATE DIAGNOSTICS KING FAISAL SPECIALIST HOSPITAL “The model was fast to use and each case took approximately less than a minute to be processed. It is expected that such a model will make an important contribution to chest imaging, especially during the current pandemic.” Dr. Riham Eiada, King Faisal Specialist Hospital
Testing for SARS-CoV-2, the virus that causes COVID-19, can be unreliable, and turnaround times for clinical samples to be sent from the field to the lab – especially from isolated rural areas – are lengthy. To add to the challenge, supply chains for the reagents needed for these tests have come under immense pressure. Governments around the world are backing point-of-care SARS-CoV-2 tests as a cost-effective and scalable means to combat the spread of the virus, but concerns have been raised about their accuracy. As Saudi Arabia looks to ramp up testing, researchers at KAUST are working on technologies that employ artificial intelligence (AI) and different sequencing methods to more quickly and accurately diagnose COVID-19 cases and suggest suitable treatments. AI-ASSISTANCE: Professor Xin Gao’s lab has been trialing an AI-based computer-aided diagnosis (CAD) system that can help detect COVID-19 infections using CT imaging. Most laboratory and point-of-care SARSCoV-2 testing relies on detecting the virus’ nucleic acid sequence, the gene sequence of this virus, or the antibody sequence produced by patients’ immune systems. This is the so-called gold standard of SARSCoV-2 diagnosis. However, as Professor Gao notes, “Clinical experience in China has shown that this type of diagnosis can produce a surprisingly high false-negative rate of between 30% and 50%.” Much of this comes down to how samples are taken, transported and stored. CT-imaging has been routinely used as one of the main diagnostic standards alongside nucleic acid detection. It can also be helpful in the prognosis and treatment stages. CT images from different stages of patients’ illnesses have very different patterns, and the early phase often
requires a high level of expertise and experience to differentiate from other lung infections. However, having radiologists analyze thousands of CT scans for COVID-19 patients is unfeasible. This is where Professor Gao’s lab comes in. Using CT scans, AI can more rapidly identify infection areas in a patient’s lungs, quantify the volume of the lung that is infected and then help clinicians decide what kind of treatment to prescribe. Though projects like these normally take years to develop, the CAD system developed at KAUST is already being trialed in several hospitals, including King Faisal Specialist Hospital in Riyadh. EASING ENZYME SHORTAGES: In the coming months, the Saudi Arabian government plans to test 14.5m people, or nearly 40% of the population. Most test kits require reverse transcriptases and DNA polymerases: chemical reagents that amplify the viral genomic RNA to detectable levels. However, surging global demand for test kits has put significant strain on the labs that produce these reagents and other enzymes. Pre-empting potential shortages, Professor Samir Hamdan’s group in the bioscience center of the Biological and Environmental Science and Engineering division has been developing reagents for SARS-CoV-2 tests for use in biomedical and clinical labs. Professor Hamdan’s group has successfully established a robust production line of DNA polymerases in their laboratory and has started working on producing reverse transcriptases. The group has also patented DNA polymerases produced from micro-organisms in the Red Sea that can work under harsh conditions. They will be using these to devise new virus detection methods that could prove vital to Saudi Arabia’s testing programs.
CLINICAL EXPERIENCE IN CHINA HAS SHOWN THAT POINT-OF-CARE DIAGNOSIS CAN PRODUCE A XIN GAO Associate Professor of Computer Science, Head of KAUST’s Structural and Functional Bioinformatics Group, and Acting Associate Director of the Computational Bioscience Research Center
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SURPRISINGLY HIGH FALSE-NEGATIVE RATE OF BETWEEN 30% AND 50%.
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