AI Model for Early Detection of SARS-CoV-2 in Children Could Pave Way for Rapid Bedside COVID-19 Diagnostic Device
|
By HospiMedica International staff writers Posted on 05 Feb 2021 |
|
Illustration
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.”
Related Links:
Wayne State University
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.”
Related Links:
Wayne State University
Gold Member
12-Channel ECG
CM1200B
New
Blood Pressure Monitor
Cuff Blood Pressure Monitor
New
Blood Gas Analyzer
i-Check200
Latest COVID-19 News
- Low-Cost System Detects SARS-CoV-2 Virus in Hospital Air Using High-Tech Bubbles
- World's First Inhalable COVID-19 Vaccine Approved in China
- COVID-19 Vaccine Patch Fights SARS-CoV-2 Variants Better than Needles
- Blood Viscosity Testing Can Predict Risk of Death in Hospitalized COVID-19 Patients
- ‘Covid Computer’ Uses AI to Detect COVID-19 from Chest CT Scans
- MRI Lung-Imaging Technique Shows Cause of Long-COVID Symptoms
- Chest CT Scans of COVID-19 Patients Could Help Distinguish Between SARS-CoV-2 Variants
- Specialized MRI Detects Lung Abnormalities in Non-Hospitalized Long COVID Patients
- AI Algorithm Identifies Hospitalized Patients at Highest Risk of Dying From COVID-19
- Sweat Sensor Detects Key Biomarkers That Provide Early Warning of COVID-19 and Flu
- Study Assesses Impact of COVID-19 on Ventilation/Perfusion Scintigraphy
- CT Imaging Study Finds Vaccination Reduces Risk of COVID-19 Associated Pulmonary Embolism
- Third Day in Hospital a ‘Tipping Point’ in Severity of COVID-19 Pneumonia
- Longer Interval Between COVID-19 Vaccines Generates Up to Nine Times as Many Antibodies
- AI Model for Monitoring COVID-19 Predicts Mortality Within First 30 Days of Admission
- AI Predicts COVID Prognosis at Near-Expert Level Based Off CT Scans
Channels
Artificial Intelligence
view channel
Machine Learning Approach Enhances Liver Cancer Risk Stratification
Hepatocellular carcinoma, the most common form of primary liver cancer, is often detected late despite targeted surveillance programs. Current screening guidelines emphasize patients with known cirrhosis,... Read more
New AI Approach Monitors Brain Health Using Passive Wearable Data
Brain health spans cognitive and emotional functions and can fluctuate even in adults without diagnosed disease. Detecting early changes remains difficult in routine care and burdens specialty services... Read more
Critical Care
view channel
Automated IV Labeling Solution Improves Infusion Safety and Efficiency
Medication administration in high-acuity settings is often complicated by multiple concurrent infusions, making accurate line identification essential. In a 10-hospital intensive care unit study, 60% of... Read more
First-Of-Its-Kind AI Tool Detects Pulmonary Hypertension from Standard ECGs
Pulmonary hypertension is a progressive, life‑threatening disease that is frequently missed early because symptoms such as dyspnea are nonspecific and diagnostic delays can exceed two years.... Read more
Surgical Techniques
view channel
Continuous Monitoring with Wearables Enhances Postoperative Patient Safety
Postoperative hypoxemia on general surgical wards is common and often missed by intermittent vital sign checks. Undetected low oxygen levels can delay recovery and raise the risk of complications that... Read more
New Approach Enables Customized Muscle Tissue Without Biomaterial Scaffolds
Volumetric muscle loss is a traumatic loss of skeletal muscle that often leads to permanent functional impairment and limited reconstructive options. Current experimental strategies struggle to deliver... Read more
Patient Care
view channel
Wearable Sleep Data Predict Adherence to Pulmonary Rehabilitation
Chronic obstructive pulmonary disease (COPD) is a long-term lung disorder that makes breathing difficult and often disturbs sleep, reducing energy for daily activities. Limited engagement in pulmonary... Read more
Revolutionary Automatic IV-Line Flushing Device to Enhance Infusion Care
More than 80% of in-hospital patients receive intravenous (IV) therapy. Every dose of IV medicine delivered in a small volume (<250 mL) infusion bag should be followed by subsequent flushing to ensure... Read more
Health IT
view channel
EMR-Based Tool Predicts Graft Failure After Kidney Transplant
Kidney transplantation offers patients with end-stage kidney disease longer survival and better quality of life than dialysis, yet graft failure remains a major challenge. Although a successful transplant... Read more
Printable Molecule-Selective Nanoparticles Enable Mass Production of Wearable Biosensors
The future of medicine is likely to focus on the personalization of healthcare—understanding exactly what an individual requires and delivering the appropriate combination of nutrients, metabolites, and... Read more
Business
view channel
GE HealthCare Leads Major European Initiative to Advance Cardio-Oncology Care
Cardiovascular complications are increasingly recognized among people undergoing or recovering from cancer treatment, driven by pre‑existing disease and the cardiotoxic effects of chemotherapy, radiotherapy,... Read more
