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Wayne State Researcher Developing AI Tool to Predict Severe COVID-19 Cases in Children

Wayne State Researcher Developing AI Tool to Predict Severe COVID-19 Cases in Children A researcher at Detroit’s Wayne State University is developing artificial intelligence to aid in the early detection of severe SARS-CoV2, the virus that causes COVID-19, in children. A Wayne State researcher is developing a device built on artificial intelligence that detects whether COVID-19 will become a more severe disease in child patients. // Image courtesy of Wayne State University A researcher at Detroit’s Wayne State University is developing artificial intelligence to aid in the early detection of severe SARS-CoV2, the virus that causes COVID-19, in children.

AI model aids early detection of severe COVID-19 infection in children

Children have been less impacted by COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SAR-CoV-2) than adults. But some children diagnosed with SARS-CoV-2 have experienced severe illnesses, including Multisystem Inflammatory Syndrome (MIS-C) and respiratory failure; nearly 80% of children with MIS-C become critically ill with a 2 to 4% mortality rate.

New AI Model for Early Detection of Severe SARS-CoV-2 Illness in Children

New AI Model for Early Detection of Severe SARS-CoV-2 Illness in Children Written by AZoRoboticsFeb 4 2021 The impact of COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on children has been less when compared to adults. Image Credit: Wayne State University. However, some children diagnosed with SARS-CoV-2 experienced acute illnesses, such as respiratory failure and Multisystem Inflammatory Syndrome (MIS-C). Almost 80% of children with MIS-C fall critically ill with a mortality rate of 2%–4%. At present, techniques to identify the spectrum of severity of the disease and to predict which children with SARS-CoV-2 exposure will develop acute illnesses, such as MIS-C, are lacking. Hence, there is an emergent need to create a diagnostic modality to differentiate the various phenotypes of the disease and for risk stratification.

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