Validation of AI-Based Detection of Idiopathic Pulmonary Fibrosis in Serial Chest Radiographs: A Retrospective Longitudinal Study

NCT07712952 | Completed

• 1 other identifier: 2507-022-19585
• Study Type: observational
• Target: 175
• Locations: 1 country
• Sites: 1

Brief Summary

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic lung disease of unknown cause with a median survival of only 3-5 years after diagnosis. Early detection and timely initiation of antifibrotic therapy may improve outcomes, but diagnosis is frequently delayed. Chest radiography (CXR) is widely accessible and cost-effective but has limited sensitivity for early interstitial opacity (IO), so radiologists may miss or delay documentation of relevant findings. This retrospective, single-center, observational cohort study evaluates whether an artificial-intelligence algorithm (VUNO Med-Chest X-ray) can detect interstitial opacity earlier than radiologists in the historical chest radiograph series of patients who were diagnosed with IPF. The cohort was identified via a April 2025 registry screening of patients carrying an IPF diagnosis at Chung-Ang University Hospital. For each patient, the date of the first AI-detected IO (using a pre-specified score cutoff) is compared with the date of the first radiologist-reported mention of interstitial/reticular opacity, across all chest radiographs obtained before the IPF diagnosis date, within a 15-year retrospective imaging window anchored to the April 2025 screening date (January 2010-April 2025). The study also explores patient characteristics that modify this lead-time difference and whether longitudinal AI IO-score trajectories are associated with mortality.

Conditions

Idiopathic Pulmonary Fibrosis; Interstitial Lung Disease

Keywords

Idiopathic Pulmonary Fibrosis; Interstitial Lung Disease; Chest Radiograph; Artificial Intelligence; Deep Learning; Lead Time; Retrospective Cohort Study; STROBE

Outcome Measures

Primary Outcomes (1)

• Paired lead-time difference (radiologist first-mention date minus AI first-detection date, days)

  Delta = radiologist\_detected\_date - ai\_detected\_date, in days. Delta greater than 0 indicates AI detected interstitial opacity earlier than the radiologist; Delta = 0 indicates same-day detection; Delta less than 0 indicates the radiologist detected it earlier. Analyzed in the paired cohort (n=166) using the Wilcoxon signed-rank test (zero differences excluded, two-sided), with effect size reported as the Hodges-Lehmann estimate and bootstrap 95% CI (4,000 resamples). Reported measures: median Delta (IQR), Hodges-Lehmann estimate (95% CI), p-value, and the proportional breakdown of AI-earlier / same-day / radiologist-earlier pairs (n, %).

  From first available chest radiograph to IPF diagnosis date (retrospective, up to 15 years, anchored to April 2025 registry screening)

Secondary Outcomes (4)

• Proportion with AI-earlier detection among discordant pairs

  From first available chest radiograph to IPF diagnosis date (retrospective, up to 15 years, anchored to April 2025 registry screening)

• Proportion detecting more than 180 days before diagnosis - AI vs. Radiologist

  From first available chest radiograph to IPF diagnosis date (retrospective, up to 15 years, anchored to April 2025 registry screening)

• Proportion detecting within 180 days before diagnosis - AI vs. Radiologist

  From first available chest radiograph to IPF diagnosis date (retrospective, up to 15 years, anchored to April 2025 registry screening)

• Sensitivity analysis of the primary lead-time comparison using alternative zero-handling methods

  From first available chest radiograph to IPF diagnosis date (retrospective, up to 15 years, anchored to April 2025 registry screening)

Other Outcomes (3)

• Kaplan-Meier Time-to-Detection Curves for AI versus Radiologist

  From first available chest radiograph to IPF diagnosis date (retrospective, up to 15 years, anchored to April 2025 registry screening)

• Time From AI First Detection to Eventual IPF Diagnosis

  From first available chest radiograph to IPF diagnosis date (retrospective, up to 15 years, anchored to April 2025 registry screening)

• Predictors of the Primary Lead-Time Difference (Regression Coefficients)

  From first available chest radiograph to IPF diagnosis date (retrospective, up to 15 years, anchored to April 2025 registry screening)

Study Arms (1)

IPF-diagnosed cohort

Patients with a final diagnosis of idiopathic pulmonary fibrosis at Chung-Ang University Hospital, identified via an April 2025 registry screening, whose historical chest radiograph series obtained before diagnosis (within a 15-year retrospective window, January 2010-April 2025) were retrospectively analyzed by both the AI algorithm and radiology reports.

Device: VUNO Med-Chest X-ray

Interventions

VUNO Med-Chest X-ray DEVICE

Retrospective, offline application of the AI-based chest radiograph analysis software VUNO Med-Chest X-ray (VUNO Inc., Seoul, Korea) to archival chest radiographs obtained before each patient's IPF diagnosis. The software outputs scores for interstitial opacity(reticular opacity), consolidation, and nodule/mass; interstitial opacity(reticular opacity) score, applying a pre-specified cutoff, is used for the primary and secondary analyses. The AI analysis is performed solely for research purposes and does not inform clinical care.

IPF-diagnosed cohort

Eligibility Criteria

• Age: 19 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adults aged 19 years or older carrying a final diagnosis of idiopathic pulmonary fibrosis (IPF; ICD-10 J84.1 or clinical diagnosis) at Chung-Ang University Hospital, identified via an April 2025 registry screening, with a digital chest radiograph series available before the diagnosis date within the 15-year retrospective imaging window (January 2010-April 2025).

You may qualify if:

• IPF diagnosis on record at Chung-Ang University Hospital as of the April 30, 2025 registry screening, based on imaging findings, pathology results, and clinical information as determined by a pulmonology specialist
• Age greater than or equal to 19 years at IPF diagnosis
• Confirmed diagnosis of IPF (by clinician or multidisciplinary discussion, including CT and/or biopsy)
• Two or more frontal (PA or AP) chest radiographs obtained before the diagnosis date
• DICOM images available and analyzable by VUNO Med-Chest X-ray
• Date of initial IPF diagnosis available

You may not qualify if:

• Only non-frontal chest radiograph views available (e.g., lateral view only)
• One or fewer analyzable chest radiographs
• Missing initial diagnosis date
• No radiology report data available for comparison

Sponsors & Collaborators

• Chung-Ang University Hospital: lead
• VUNO Inc.: collaborator

Study Sites (1)

Chung-Ang University Hospital

Seoul, South Korea

Location

MeSH Terms

Conditions

Idiopathic Pulmonary Fibrosis; Lung Diseases, Interstitial

Condition Hierarchy (Ancestors)

Pulmonary Fibrosis; Lung Diseases; Respiratory Tract Diseases

Study Officials

• Kyoungmin Moon

  Chung-Ang University Hospital

  PRINCIPAL INVESTIGATOR

• Yoona Hwang

  VUNO Inc.

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate professor of Pulmonary and Allergy Medicine

Study Record Dates

• First Submitted: July 2, 2026
• First Posted: July 20, 2026
• Study Start: April 30, 2025
• Primary Completion: April 30, 2025
• Study Completion: April 30, 2025
• Last Updated: July 20, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

KR (1)