Lung Disease, InterstitialNCT07686562
Artificial Intelligence (AI) Detection of Incidental Interstitial Opacity on Chest Radiography
An observational study of Lung Disease, Interstitial and Idiopathic Pulmonary Fibrosis (IPF), run by Chung-Ang University Hospital at 1 site in South Korea.
Trial summary
The goal of this observational study is to learn how well an artificial intelligence (AI)-based chest X-ray analysis software can incidentally detect interstitial lung disease (ILD), which appears as interstitial opacity, on chest X-rays taken for other reasons, and whether these AI-flagged findings represent true… Read more about this study
- Who is it for?
- Ages 19 years and olderHealthy volunteers can take part
- Conditions the study is for
- Lung Disease, Interstitial, Idiopathic Pulmonary Fibrosis (IPF), Incidental Findings
- What is being tested?
- VUNO Med®-Chest X-ray™
- Type of treatment
- Medical device
- How many people
- 1,293 participants
- Where
- 1 siteSouth Korea
- Study dates
- 1 Feb 2022to 31 Dec 2024
- 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
- Healthy volunteers accepted
- Adult, Older adult
Study population: Adults aged ≥19 years attending the pulmonology and allergy clinics of Chung-Ang University Hospital (Seoul and Gwangmyeong, Republic of Korea) who underwent chest radiography between January 2022 and December 2024.
Who may be able to join
- Adults aged 19 years or older
- Visited the pulmonology and allergy clinic (outpatient or inpatient) at Chung-Ang University Hospital (Seoul or Gwangmyeong) and underwent chest radiography from January 2022 to December 2024
- A follow-up CT performed after the index chest radiograph
- Reticular/interstitial opacity detected on the index radiograph by VUNO Med®-Chest X-ray™
Who cannot join
- Prior history of ILD or ILD-related disease before the index chest radiograph, or a CT report containing terms related to interstitial opacity
- Non-frontal (non-posteroanterior/anteroposterior [PA/AP]) chest radiograph view position
- Missing CT report or final clinical diagnosis
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Evaluating the Real-World Performance of Artificial Intelligence (AI)-Based Detection for Interstitial Lung Disease in Chest X-Ray Images
The goal of this observational study is to learn how well an artificial intelligence (AI)-based chest X-ray analysis software can incidentally detect interstitial lung disease (ILD), which appears as interstitial opacity, on chest X-rays taken for other reasons, and whether these AI-flagged findings represent true interstitial opacity.
The main question it aims to answer is: How often does an AI-flagged interstitial opacity correspond to true ILD?
This retrospective study uses existing records: researchers review each participant's follow-up computed tomography(CT), CT report, and final diagnosis to confirm true ILD and reticular opacity.
- Conditions
- Lung Disease, Interstitial, Idiopathic Pulmonary Fibrosis (IPF), Incidental Findings, Interstitial Lung Disease (ILD), Chest X-ray for Clinical Evaluation
- Being tested
- VUNO Med®-Chest X-ray™
- Medical subject headings
- Lung Diseases, Interstitial, Idiopathic Pulmonary Fibrosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
VUNO Med®-Chest X-ray™Device
VUNO Med®-Chest X-ray™ is artificial intelligence (AI)-based software that supports the detection and diagnosis of abnormal findings on chest radiographs. It automatically identifies abnormal findings and provides information on their type and location to aid clinical decision-making.
| Study group | Role | What they receive |
|---|---|---|
| AI interstitial opacity-positive | Patients whose index chest radiograph was flagged as interstitial opacity-positive by the AI software. Receives: Device: VUNO Med®-Chest X-ray™ |
What is being measured
The primary outcome is the main question the study is designed to answer.
Positive predictive value (PPV) of AI-detected interstitial opacity
Positive predictive value (PPV) of the AI flag for interstitial opacity is the proportion of AI interstitial-opacity-positive index radiographs confirmed as true positives by the radiologist reference standard (consensus review of the paired follow-up CT, CT report, follow-up diagnoses, and the index radiograph). PPV = true positives / all AI interstitial-opacity-positive cases.
Measured: From the index chest radiograph to the reference standard confirmation (the first follow-up CT after the index chest radiograph and/or final clinical diagnosis), up to 3.5 years
Secondary outcomes (1)
Comparison of AI finding scores between true-positive and false-positive cases
The AI finding scores (e.g., interstitial opacity, consolidation, nodule, pleural effusion) were compared between interstitial-opacity true-positive and false-positive cases using the Mann-Whitney U test. Scores are summarized as the median (first-third quartile, Q1-Q3).
Measured: From the index chest radiograph to the reference standard confirmation (the first follow-up CT after the index chest radiograph and/or final clinical diagnosis), up to 3.5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Jul 2026
- Study start1 Feb 2022
- Primary completion31 Dec 2024
- Study completion31 Dec 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung Disease, InterstitialStudies 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
- 1,293 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
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07686562
- Sponsor's protocol ID
- 2411-003-19548
- Status last verified
- Jul 2026
- First submitted
- 25 Jun 2026
- Last updated
- 7 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 7 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating the Real-World Performance of Artificial Intelligence (AI)-Based Detection for Interstitial Lung Disease in Chest X-Ray Images
- ClinicalTrials.gov ID
- NCT07686562
- Acronym / sponsor ID
- 2411-003-19548
- Conditions
- Lung Disease, Interstitial, Idiopathic Pulmonary Fibrosis (IPF), Incidental Findings, Interstitial Lung Disease (ILD), Chest X-ray for Clinical Evaluation
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,293 participants (actual)
- Lead sponsor
- Chung-Ang University HospitalUniversity, hospital or other
- Collaborators
- VUNO Inc.
- Study start
- 1 Feb 2022 (actual)
- Primary completion
- 31 Dec 2024 (actual)
- Study completion
- 31 Dec 2024 (actual)
- First posted
- 7 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.
The goal of this observational study is to learn how well an artificial intelligence (AI)-based chest X-ray analysis software can incidentally detect interstitial lung disease (ILD), which appears as interstitial opacity, on chest X-rays taken for other reasons, and whether these AI-flagged findings represent true interstitial opacity.
The main question it aims to answer is: How often does an AI-flagged interstitial opacity correspond to true ILD?
This retrospective study uses existing records: researchers review each participant's follow-up computed tomography(CT), CT report, and final diagnosis to confirm true ILD and reticular opacity.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AI interstitial opacity-positive | – | Device: VUNO Med®-Chest X-ray™Patients whose index chest radiograph was flagged as interstitial opacity-positive by the AI software. |
Who may qualify
- Ages
- 19 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Adults aged ≥19 years attending the pulmonology and allergy clinics of Chung-Ang University Hospital (Seoul and Gwangmyeong, Republic of Korea) who underwent chest radiography between January 2022 and December 2024.
Key inclusion criteria (4 listed)
- Adults aged 19 years or older
- Visited the pulmonology and allergy clinic (outpatient or inpatient) at Chung-Ang University Hospital (Seoul or Gwangmyeong) and underwent chest radiography from January 2022 to December 2024
- A follow-up CT performed after the index chest radiograph
- Reticular/interstitial opacity detected on the index radiograph by VUNO Med®-Chest X-ray™
Key exclusion criteria (3 listed)
- Prior history of ILD or ILD-related disease before the index chest radiograph, or a CT report containing terms related to interstitial opacity
- Non-frontal (non-posteroanterior/anteroposterior [PA/AP]) chest radiograph view position
- Missing CT report or final clinical diagnosis
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-ray™VUNO Med®-Chest X-ray™ is artificial intelligence (AI)-based software that supports the detection and diagnosis of abnormal findings on chest radiographs. It automatically identifies abnormal findings and provides information on their type and location to aid clinical decision-making. | Device | AI interstitial opacity-positive |
Primary outcome
Positive predictive value (PPV) of AI-detected interstitial opacity
Time frame: From the index chest radiograph to the reference standard confirmation (the first follow-up CT after the index chest radiograph and/or final clinical diagnosis), up to 3.5 years
Positive predictive value (PPV) of the AI flag for interstitial opacity is the proportion of AI interstitial-opacity-positive index radiographs confirmed as true positives by the radiologist reference standard (consensus review of the paired follow-up CT, CT report, follow-up diagnoses, and the index radiograph). PPV = true positives / all AI interstitial-opacity-positive cases.
1 secondary outcome
Comparison of AI finding scores between true-positive and false-positive cases
Time frame: From the index chest radiograph to the reference standard confirmation (the first follow-up CT after the index chest radiograph and/or final clinical diagnosis), up to 3.5 years
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 7 Jul 2026.
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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.
The record lists no contact. Use the official record to reach the sponsor.
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 Lung Disease, Interstitial and Idiopathic Pulmonary Fibrosis (IPF), 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 4 inclusion and 3 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 7 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?
Positive predictive value (PPV) of AI-detected interstitial opacity (time frame: From the index chest radiograph to the reference standard confirmation (the first follow-up CT after the index chest radiograph and/or final clinical diagnosis), up to 3.5 years). 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 7 Jul 2026.