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Application of deep learning in the identification of CT pulmonary infection

Artificial intelligence medical imaging analysis of pulmonary infection

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400092812
Enrollment
Unknown
Registered
2024-11-25
Start date
2024-11-25
Completion date
Unknown
Last updated
2024-12-02

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

Conditions

pulmonary infection

Interventions

bacterial pneumonia:NA
viral pneumonia:NA
fungal pneumonia:NA

Sponsors

The first affiliatted hospital of Soochow University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1. The age of 18 years old or more; 2. Viral infection confirmed by microbiological experiments; Fungal infection was diagnosed by galactomannan test and clinical diagnostic criteria. 3. Patients with bacterial infections, there is a clear etiology evidence (e.g., streptococcus pneumoniae, legionella pneumonia); Or meet the diagnostic criteria of community-acquired pneumonia, the pathogenic results have diagnostic value for bacterial pneumonia, no evidence of Mycoplasma pneumoniae or Chlamydia pneumoniae infection, and the effect of antibiotic treatment is clear. 4. Patients who received chest CT scan before treatment; 5. With a thin layer of medical digital imaging and communication (DICOM) format of the CT images of patients.

Exclusion criteria

Exclusion criteria: 1. Patients with other lung diseases (including pneumonia with more than two pathogens or tumors); 2. Patients with a history of pulmonary surgery; 3. Poor CT image quality due to respiratory motion or metal artifacts.

Design outcomes

Primary

MeasureTime frame
Area Under the Curve;Accuracy;Precision;Recall;F1;

Secondary

MeasureTime frame
Sensitivity;Specificity;

Countries

China

Contacts

Public ContactXin Wang

First Affiliated Hospital of SooChow University

sdfyywangxin@126.com+86 188 9654 4002

Outcome results

None listed

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