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Development and Application of an AI-Driven Acoustic Model for Early Detection of Common Pediatric Respiratory Tract Infections

Development and Application of an AI-Driven Acoustic Model for Early Detection of Common Pediatric Respiratory Tract Infections

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500100655
Enrollment
Unknown
Registered
2025-04-13
Start date
2025-04-20
Completion date
Unknown
Last updated
2025-04-21

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Diagnosis of respiratory systems such as pneumonia, pertussis, bronchitis, upper respiratory tract infections, rhinitis/sinusitis, etc

Interventions

Pertussis group:None
Rhinitis/sinusitis group:None
Control group:None
Other upper respiratory tract infection group:None

Sponsors

Children's Hospital,Zhejiang University School of Medicine
Lead Sponsor

Eligibility

Sex/Gender
All
Age
No minimum to 18 Years

Inclusion criteria

Inclusion criteria: 1.Inclusion criteria for respiratory system group: Children diagnosed clinically with pneumonia, pertussis, bronchitis, upper respiratory tract infection, rhinitis/sinusitis in outpatient electronic medical records (EHR). 2.Inclusion criteria for the health group: healthy individuals without clinical symptoms in the physical examination center.

Exclusion criteria

Exclusion criteria: 1.People who are unable to cough voluntarily; 2.Coughs less than 50 decibels; 3.Those who refused to participate in this trial;

Design outcomes

Primary

MeasureTime frame
Cough decibels;

Secondary

MeasureTime frame
laboratory data;

Countries

China

Contacts

Public ContactWang Hua

Children's Hospital,Zhejiang University School of Medicine

Huawang02@126.com+86 139 6716 2913

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026