Covid-19 diagnostic testing – Wu Feng

Our project seeks to mitigate the COVID-19 outbreak with early and accurate testing of all suspected COVID-19 persons and early and accurate monitoring of COVID-19 patients to facilitate appropriate treatment. However, the current genetic-based test (i.e., RT-PCR) for COVID-19 involves…

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Our project seeks to mitigate the COVID-19 outbreak with early and accurate testing of all suspected COVID-19 persons and early and accurate monitoring of COVID-19 patients to facilitate appropriate treatment. However, the current genetic-based test (i.e., RT-PCR) for COVID-19 involves many different materials, e.g., swabs, tubes, and chemical solutions, of which certain ones are in short supply at different times in different places across the USA. Furthermore, the genetic-based test results in a high false negative rate of 30%-40% (i.e., approximately one in three tests results in a negative but the person is actually positive). Thus, this project is delivering an alternative COVID-19 testing and monitoring capability that can be widely available and deliver results in minutes with high accuracy. How? By realizing, deploying, and continually improving our Compute-COVID19 software tool to facilitate early and accurate testing and monitoring of COVID-19 via post-image boosting and analysis of computed tomography (CT) scans, which use computer-processed combinations of many X-ray measurements to produce cross-section images of the lungs to facilitate accurate COVID-19 diagnosis. The project leverages and extends recent advances in artificial intelligence (AI) and high-performance computing (HPC) to create a high-performance software tool to significantly enhance the quality of chest CT images, which, in turn, facilitate more accurate analysis and identification of the hallmark features of COVID-19, including consolidation, bilateral and peripheral disease, linear opacities, “crazy-paving” pattern, and the “reverse halo” sign. Specifically, our novel deep-learning neural network enhances the resolution and reduces the artifacts of chest CT images by modeling the image-formation processes in chest CT to deliver a super-resolution and deblur-based iterative framework for CT images resulting in a highly accurate and highly available test for the rapid diagnosis and monitoring of COVID-19.

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