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Machine learning-based CT texture analysis for differential diagnosis of pulmonary infections in immunocompromised patients

Machine learning-based CT texture analysis for differential diagnosis of pulmonary infections in immunocompromised patients

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000038557
Enrollment
Unknown
Registered
2020-09-24
Start date
2017-10-01
Completion date
Unknown
Last updated
2020-11-30

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

Conditions

pulmonary infections

Interventions

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Sponsors

Nanfang Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 65 Years

Inclusion criteria

Inclusion criteria: 1. Identified by the hematology departments, host factors (EORTC); 2. Aged 18-65 years; 3. Suspected of pulmonary infections, clinical symptoms and signs, including persistent cough, pleural pain, or hemoptysis; 4. CT slice thickness: 0.925mm-1.25mm; 5. CT findings segmental or lobar consolidation.

Exclusion criteria

Exclusion criteria: Inadequate image quality for CT diagnosis.

Design outcomes

Primary

MeasureTime frame
Accuracy;Sensitivity;Specificity;AUC;—

Countries

China

Contacts

Public ContactYikai Xu

Nanfang Hospital

yikaixu917@gmail.com+86 02061642086

Outcome results

None listed

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