Idiopathic Pulmonary Fibrosis, Interstitial Lung Disease
Conditions
Keywords
Idiopathic Pulmonary Fibrosis, Interstitial Lung Disease, Chest Radiograph, Artificial Intelligence, Deep Learning, Lead Time, Retrospective Cohort Study, STROBE
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.
Detailed description
Background: IPF is an interstitial lung disease with a median survival of 3-5 years following diagnosis. Because interstitial opacity on CXR is subtle in early disease, opportunistic AI-based detection during CXR obtained for unrelated indications (emergency, gastroenterology, cardiology, etc.) could substantially shorten the time to diagnosis. Prior AI-CXR research has largely validated single-timepoint detection performance for findings such as pneumothorax, nodules, and pleural effusion; few studies have quantified how much earlier an AI system can detect interstitial opacity compared with radiologist reporting across a patient's full longitudinal CXR history. Objectives: 1. Quantify the lead-time advantage of AI (VUNO Med-Chest X-ray) versus radiologist reporting for first detection of interstitial opacity in the retrospective CXR series of IPF patients. 2. Compare the proportion of early detections (greater than 180 days before diagnosis) between AI and radiologists. 3. Explore patient-level effect modifiers of the AI-radiologist lead-time difference (follow-up duration, number of CXRs, prior emphysema/pneumonia history, prior CT availability, CPFE/COPD mention, pulmonology/allergy visit history). 4. Examine whether longitudinal AI IO-score trajectory patterns (progressive, oscillating, persistently high) are associated with mortality using Cox proportional-hazards modeling. Design and methods: Retrospective, single-center, observational cohort study conducted at Chung-Ang University Hospital (Seoul, Republic of Korea), reported in accordance with STROBE reporting guidelines. The cohort was identified via an April 2025 registry screening of patients carrying an IPF diagnosis at Chung-Ang University Hospital. All available frontal (PA or AP) chest radiographs obtained before each patient's IPF diagnosis date, within a 15-year retrospective imaging window anchored to the April 2025 screening date (January 2010-April 2025), are analyzed with VUNO Med-Chest X-ray (interstitial opacity, consolidation, nodule/mass) and compared against the corresponding radiology reports. Vital status (mortality) was ascertained as of the April 2025 screening date, with no follow-up beyond that cutoff. Index test: VUNO Med-Chest X-ray interstitial opacity (IO) score, using a pre-specified cutoff (0.35). The AI-detected date is defined as the date of the earliest pre-diagnosis CXR meeting this cutoff. Comparator: The radiologist-detected date is defined as the earliest chest-radiograph report date containing terminology consistent with reticular pattern / interstitial opacity(reticular opacity) / IO. A sensitivity analysis separately accounts for reports where a chest CT was obtained within +/- 3 months, given the potential influence of CT findings on radiologist reporting. Of 175 enrolled patients with a confirmed IPF diagnosis and an available pre-diagnosis CXR series, 166 comprise the primary paired analysis cohort (AI and radiologist detection dates both available before diagnosis).
Interventions
DEVICEVUNO Med-Chest X-ray
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.
Sponsors
Chung-Ang University Hospital
VUNO Inc.
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
19 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 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
Exclusion criteria
* 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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Paired lead-time difference (radiologist first-mention date minus AI first-detection date, days) | From first available chest radiograph to IPF diagnosis date (retrospective, up to 15 years, anchored to April 2025 registry screening) | 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, %). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 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) | Exact binomial sign test (null hypothesis p=0.5, two-sided); reported as the AI-earlier proportion (n/N, %) and p-value, assessed among the subset of the paired cohort with a non-zero lead-time difference (n=73). |
| 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) | Exact McNemar test plus continuity-corrected asymptotic McNemar test on a 2x2 paired table; reported measures: each proportion (n, %, 95% CI), number of discordant pairs, and p-value. Assessed in the full enrolled cohort (n=175). |
| 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) | Exact McNemar test plus continuity-corrected asymptotic McNemar test on a 2x2 paired table; reported measures: each proportion (n, %, 95% CI), number of discordant pairs, and p-value. Assessed in the full enrolled cohort (n=175). |
| 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) | Wilcoxon signed-rank test repeated with three zero-handling methods (wilcox: zeros excluded; pratt: zeros included in ranking then removed; zsplit: zero ranks split); statistic and p-value reported for each method. Assessed in the primary paired analysis cohort (n=166). |
Countries
South Korea
Contacts
PRINCIPAL_INVESTIGATORKyoungmin Moon
Chung-Ang University Hospital
PRINCIPAL_INVESTIGATORYoona Hwang
VUNO Inc.
Outcome results
None listed