AI Model for Early Detection of SARS-CoV-2 in Children Could Pave Way for Rapid Bedside COVID-19 Diagnostic Device

An artificial intelligence (AI) model to aid in the early detection of severe SARS-CoV2 illness in children is expected to improve outcomes via early recognition, timely intervention and appropriate allocation of critical resources, as well as lead to the development of a rapid bedside COVID-19 diagnostic device.

To prevent children from becoming critically ill from SARS-CoV-2, a team of researchers at Wayne State University (Detroit, MI, USA) is working to define and compare the salivary molecular host response in children with varying phenotypes of SARS-CoV-2 infections and develop and validate a sensitive and specific model to predict severe SARS-CoV-2 illness in children. They are working to develop a portable, rapid device that quantifies salivary miRNAs with comparable accuracy to predicate technology (qRT-PCR). The team will develop an AI-assisted cloud and mobile system for early recognition of severe SARS-CoV-2 infection in children.

Currently, there are no methods to discern the spectrum of the disease’s severity and predict which children with SARS-CoV-2 exposure will develop severe illness, including Multisystem Inflammatory Syndrome (MIS-C). Because of this, there is an urgent need to develop a diagnostic modality to distinguish the varying phenotypes of disease and risk stratify disease. The research team aims to develop an innovative and efficient AI model with cloud and edge intelligence-integrating non-invasive biomarkers with social determinants of health and clinical data to aid with early detection of severe SARS-CoV-2 illness in children.

“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,” said Dongxiao Zhu, Ph.D., associate professor of computer science in the College of Engineering, who is leading the study. “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.”


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