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Single Time Point Prediction as Earlier Diagnosis of Progressive Pulmonary Fibrosis
Sponsor: University of California, Los Angeles
Summary
This study is a prospective observational study for subjects with idiopathic pulmonary fibrosis (IPF) or non-IPF interstitial lung diseases (ILD). The purpose of this study is to compare whether imaging patterns from high-resolution computed tomography (HRCT) at baseline can predict worsening. Single Time point Prediction (STP) is a score derived from an artificial intelligenc/ machine learning (AI/ML) using the radiomic features from a HRCT scan that quantifies the imaging patterns of short-term predictive worsening.
Official title: Imaging Signature of Progressive Pulmonary Fibrosis in Idiopathic Pulmonary Fibrosis and Non-IPF Interstitial Lung Diseases
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
200
Start Date
2024-11-06
Completion Date
2028-08-19
Last Updated
2025-06-15
Healthy Volunteers
No
Conditions
Locations (1)
UCLA
Los Angeles, California, United States