Single Time Point Prediction as Earlier Diagnosis of Progressive Pulmonary Fibrosis

Recruiting now

Conditions studied: Pulmonary Fibrosis

In brief

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.

Key facts

Study ID
NCT06162884
Run by
University of California, Los Angeles
People needed
200
Starts
2024-11-06
Expected to finish
2029-08-19
Last updated by the study team
2026-06-18

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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