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.
Dongxiao Zhu, associate professor of computer science in Wayne State’s College of Engineering, is working with researchers from Central Michigan University in Mount Pleasant and Penn State University in Pennsylvania, to define and compare the salivary molecular host response in children with varying types of SARS-CoV-2 illness.
Children have been less impacted by COVID-19 than adults, but some children diagnosed with the virus have experienced severe illnesses, including Multisystem Inflammatory Syndrome and respiratory failure. Nearly 80 percent of children with Multisystem Inflammatory Syndrome become critically ill, with a 2-4 percent mortality rate.
There are currently no methods to discern the spectrum of the disease’s severity and predict which children with COVID-19 will develop severe illness. The research team is working to develop a diagnostic tool to distinguish the different types of disease.
The device is expected to be a portable rapid-test device that quantifies salivary RNA molecules. Zhu and his team will develop an artificial intelligence-assisted cloud and mobile system for early recognition of severe SARS-CoV2 infection in children.
Severe disease is challenging to discern given the low rate of occurrence, spectrum of symptoms that mimic other common infections, lag period before the development of severe illness, and the absence of a sensitive diagnostic tool. This has led to a need to develop a sensitive, noninvasive, and rapid system to predict severe illness.
“Our research is critical as we expect to improve outcomes of children with severe SARS-CoV-2 infection via early recognition, timely intervention, and appropriate allocation of critical resources,” Zhu says. “The successful completion of the project will also be significant, as it will lead to the development of a rapid bedside diagnostic device and creation of patient profiles based on individual risk factors, which we expect to lead to personalized treatments in the future.”
The two-year project received more than $1.4 million from the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health, which awarded eight research grants to develop approaches for identifying children at high risk for Multisystem Inflammatory Syndrome. Up to $20 million will be provided for the projects over four years, pending availability of the funds.
The project is titled “Severity Predictors Integrating Salivary Transcriptomics and Proteomics with Multi Neural Network Intelligence in SARS-CoV2 Infection in Children.”