Children have been less impacted by COVID-19 caused by the severe acute respiratory SARS-CoV-2 than adults. However, some children diagnosed with SARS-CoV-2 have experienced severe illnesses including MIS-C and respiratory failure as nearly 80% of children with MIS-C have become critically ill with a 2 to 4% mortality rate.
To prevent children from becoming critically ill from SARS-CoV-2, researchers at Wayne State University, https://www.research.wayne.edu led by Dongxiao Zhu PhD, Associate Professor of Computer Science in the College of Engineering, are developing an Artificial Intelligence (AI) model to help aid in the early detection of severe SARS-CoV2 in children.
The team aims to develop an innovative and efficient AI model with cloud and edge intelligence-integrating noninvasive biomarkers with social determinants of health and clinical data.
The two year project, Severity Predictors Integrating Salivary Transcriptomics and proteomics with Multi Neural Network Intelligence in SARS-CoV2 Infection in Children (SPITS MISC) received $1,433,469 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development https://www.nichd.nih.gov within NIH.
In total NIH awarded eight research grants to develop approaches for identifying children at high risk for MIS-C. Up to $20 million will be provided over the next four years pending the availability of funds.