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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. ....

United States , Wayne State University , Penn State University , Dongxiao Zhu , Multi Neural Network Intelligence , Wayne State College Of Engineering , Central Michigan University In Mount Pleasant , Detroit Wayne State University , Wayne State , Central Michigan University , Mount Pleasant , Multisystem Inflammatory Syndrome , Eunice Kennedy Shriver National Institute , Child Health , Human Development , National Institutes , Multisystem Inflammatory , Predictors Integrating Salivary Transcriptomics , ஒன்றுபட்டது மாநிலங்களில் , வேன் நிலை பல்கலைக்கழகம் , பென் நிலை பல்கலைக்கழகம் , பல நரம்பியல் வலைப்பின்னல் உளவுத்துறை , வேன் நிலை கல்லூரி ஆஃப் பொறியியல் , மைய மிச்சிகன் பல்கலைக்கழகம் இல் ஏற்ற ப்லெஸெஂட் , டெட்ராய்ட் வேன் நிலை பல்கலைக்கழகம் , வேன் நிலை ,

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. ....

Dongxiao Zhu , Usha Sethuraman , Steven Hicks , Wayne State University , Multi Neural Network Intelligence , Central Michigan University , Penn State University Hershey Medical Center , College Of Engineering , National Institutes Of Health , Penn State University , Wayne State , Multisystem Inflammatory Syndrome , Associate Professor , Computer Science , Predictors Integrating Salivary Transcriptomics , Eunice Kennedy Shriver National Institute , Child Health , Human Development , National Institutes , உஷா சேதுராமன் , ஸ்டீவன் ஹிக்ஸ் , வேன் நிலை பல்கலைக்கழகம் , பல நரம்பியல் வலைப்பின்னல் உளவுத்துறை , மைய மிச்சிகன் பல்கலைக்கழகம் , பென் நிலை பல்கலைக்கழகம் ஹெர்ஷே மருத்துவ மையம் , கல்லூரி ஆஃப் பொறியியல் ,