FindTrial
Sign In

Research on Monitoring and Identification of Abnormalities in Pulmonary Function and Respiratory Diseases

Research on Anomaly Detection and Identification of Respiratory Diseases in Pulmonary Function Based on Deep Learning

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500113378
Enrollment
Unknown
Registered
2025-11-27
Start date
2025-12-01
Completion date
Unknown
Last updated
2025-12-01

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

Conditions

Respiratory Diseases Related to Lung Function (including COPD and Asthma)

Interventions

Acute exacerbation of COPD:NA
Acute asthma attack:NA

Sponsors

Huzhou Center Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. Patients must be 18 years of age or older. COPD exacerbations are confirmed by pulmonary function testing (FEV1/FVC = 12%). 2. No gender requirements; 3. Respiratory rate and fluctuation monitoring data conform to standard formats (including continuous timestamps, respiratory rate values, and fluctuation amplitude parameters), collected by hospital-approved portable respiratory monitors or bedside monitors. 4. Respiratory monitoring data collection duration >= 2 hours, data validity rate (no missing data, no abnormal interference segments) >= 90%, monitoring device sampling frequency >= 1 time/minute.

Exclusion criteria

Exclusion criteria: 1.Poor breathing coordination resulted in suboptimal image quality.

Design outcomes

Primary

MeasureTime frame
Abnormal Segment Matching Degree;F1 Score;Accuracy;—

Countries

China

Contacts

Public ContactFeng Hua

Huzhou Center Hospital

fan9010@msn.com+86 572 281 7185

Outcome results

None listed

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