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Deep Learning- and Radiomics- based Diagnosis of Early Idiopathic Pulmonary Fibrosis

Deep Learning- and Radiomics- based Diagnosis of Early Idiopathic Pulmonary Fibrosis

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500096539
Enrollment
Unknown
Registered
2025-01-26
Start date
2025-02-06
Completion date
Unknown
Last updated
2025-02-10

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

Conditions

Idiopathic Pulmonary Fibrosis

Interventions

Gold Standard:Multidisciplinary discussion
Index test:Unimodal predictive models constructed based on chest CT Multimodal predictive models constructed based on chest CT, histopathology and clinical data

Sponsors

Department of Pulmonary and Critical Care Medicine,Shanghai Chest Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 100 Years

Inclusion criteria

Inclusion criteria: 1) Histopathological evidence of UIP or possible UIP, and exclusion of other known causes. 2) No histopathology, chest CT shows UIP or probable UIP, and exclusion of other known causes.

Exclusion criteria

Exclusion criteria: 1) Patients with a clear history of exposure or a diagnosis of autoimmune disease. 2) Patients with poorly defined CT images that severely compromise assessment.

Design outcomes

Primary

MeasureTime frame
accuracy;specificity;sensitivity;

Countries

China

Contacts

Public ContactFeng Li

Department of Pulmonary and Critical Care Medicine,Shanghai Chest Hospital,Shanghai,200030, P.R. China

lifeng741@aliyun.com+86 137 6130 6949

Outcome results

None listed

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