Idiopathic Pulmonary FibrosisNCT07712952
Validation of AI-Based Detection of Idiopathic Pulmonary Fibrosis in Serial Chest Radiographs: A Retrospective Longitudinal Study
An observational study of Idiopathic Pulmonary Fibrosis and Interstitial Lung Disease, run by Chung-Ang University Hospital at 1 site in South Korea.
Trial 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. Read more about this study
- Who is it for?
- Ages 19 years and older
- Conditions the study is for
- Idiopathic Pulmonary Fibrosis, Interstitial Lung Disease
- What is being tested?
- VUNO Med-Chest X-ray
- Type of treatment
- Medical device
- How many people
- 175 participants
- Where
- 1 siteSouth Korea
- Study dates
- 30 Apr 2025to 30 Apr 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 19 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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).
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Retrospective Evaluation of AI-Based Early Detection of Reticular Opacity in Longitudinal Chest Radiograph Sequences in Patients With Idiopathic Pulmonary Fibrosis
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.
Read the 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:
- 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.
- Compare the proportion of early detections (greater than 180 days before diagnosis) between AI and radiologists.
- 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).
- 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).
- Being tested
- VUNO Med-Chest X-ray
- Keywords
- Idiopathic Pulmonary Fibrosis, Interstitial Lung Disease, Chest Radiograph, Artificial Intelligence, Deep Learning, Lead Time, Retrospective Cohort Study, STROBE
- Medical subject headings
- Idiopathic Pulmonary Fibrosis, Lung Diseases, Interstitial
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
VUNO Med-Chest X-rayDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Device: VUNO Med-Chest X-ray |
What is being measured
The primary outcome is the main question the study is designed to answer.
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, %).
Measured: 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
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).
Measured: 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
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).
Measured: 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
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).
Measured: 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
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).
Measured: From first available chest radiograph to IPF diagnosis date (retrospective, up to 15 years, anchored to April 2025 registry screening)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Jul 2026
- Study start30 Apr 2025
- Primary completion30 Apr 2025
- Study completion30 Apr 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Idiopathic Pulmonary FibrosisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 175 participants (actual)
Sponsor and investigators
- Lead sponsor
- Chung-Ang University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- VUNO Inc.
- Responsible party
- Principal investigator, Kyoungmin Moon, Chung-Ang University Hospital
- Investigators
- Kyoungmin Moon, Principal investigator, Chung-Ang University HospitalYoona Hwang, Principal investigator, VUNO Inc.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07712952
- Sponsor's protocol ID
- 2507-022-19585
- Status last verified
- Jul 2026
- First submitted
- 2 Jul 2026
- Last updated
- 20 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: This is a retrospective study using data collected under an IRB-approved waiver of informed consent. Individual participant data were not collected with participant consent for sharing with third parties, and no data-sharing infrastructure or de-identification protocol for external release has been established.
Study at a glance
From the registry record, last updated by the sponsor on 20 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Retrospective Evaluation of AI-Based Early Detection of Reticular Opacity in Longitudinal Chest Radiograph Sequences in Patients With Idiopathic Pulmonary Fibrosis
- ClinicalTrials.gov ID
- NCT07712952
- Acronym / sponsor ID
- 2507-022-19585
- Keywords
- Idiopathic Pulmonary Fibrosis, Interstitial Lung Disease, Chest Radiograph, Artificial Intelligence, Deep Learning, Lead Time, Retrospective Cohort Study, STROBE
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 175 participants (actual)
- Lead sponsor
- Chung-Ang University HospitalUniversity, hospital or other
- Collaborators
- VUNO Inc.
- Study start
- 30 Apr 2025 (actual)
- Primary completion
- 30 Apr 2025 (actual)
- Study completion
- 30 Apr 2025 (actual)
- First posted
- 20 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- 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.
- Compare the proportion of early detections (greater than 180 days before diagnosis) between AI and radiologists.
- 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).
- 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).
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| IPF-diagnosed cohort | – | Device: VUNO Med-Chest X-rayPatients 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. |
Who may qualify
- Ages
- 19 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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).
Key inclusion criteria (6 listed)
- 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
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| VUNO Med-Chest X-rayRetrospective, 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. | Device | IPF-diagnosed cohort |
Primary outcome
Paired lead-time difference (radiologist first-mention date minus AI first-detection date, days)
Time frame: 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, %).
4 secondary outcomes
Proportion with AI-earlier detection among discordant pairs
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: From first available chest radiograph to IPF diagnosis date (retrospective, up to 15 years, anchored to April 2025 registry screening)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Jul 2026.
- Individual participant dataSharing planned: No. This is a retrospective study using data collected under an IRB-approved waiver of informed consent. Individual participant data were not collected with participant consent for sharing with third parties, and no data-sharing infrastructure or de-identification protocol for external release has been established.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Kyoungmin Moon principal investigator
Chung-Ang University Hospital
Yoona Hwang principal investigator
VUNO Inc.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Idiopathic Pulmonary Fibrosis and Interstitial Lung Disease, run by Chung-Ang University Hospital at 1 site in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 20 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Paired lead-time difference (radiologist first-mention date minus AI first-detection date, days) (time frame: From first available chest radiograph to IPF diagnosis date (retrospective, up to 15 years, anchored to April 2025 registry screening)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Jul 2026.