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AI outruns radiologists in detecting early lung scarring

NCT ID NCT07712952

Knowledge-focused Sponsor: Chung-Ang University Hospital Source: ClinicalTrials.gov ↗

What the study statuses mean

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Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed This study
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Jul 20, 2026 · Last updated Jul 21, 2026 · Updated 1 time

Summary

This study looks at whether an artificial intelligence program can detect early signs of idiopathic pulmonary fibrosis (IPF) on chest X-rays earlier than radiologists. IPF is a serious lung disease that causes scarring and is often diagnosed late. Researchers will compare the date the AI first spots a subtle lung abnormality with the date a radiologist first reported it, using historical X-rays from 175 patients. The goal is to see if AI can give an earlier warning, which could lead to faster treatment.

What this could mean

Our plain-language read of the trial. This is informational only, not medical advice or a prediction.

Active substance
VUNO Med-Chest X-ray, an AI software that analyzes chest X-rays for signs of lung disease
What this could lead to
If successful, this AI could help doctors diagnose idiopathic pulmonary fibrosis earlier, potentially leading to earlier treatment and better outcomes for patients.
What could go wrong
This is a retrospective study using historical data, so the AI's performance in real-time clinical practice may differ. The study is also small and single-center, so results may not apply broadly.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Participants

175 people

The number who actually took part.

Started

Apr 2025

Finished

Apr 2025

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

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).

Ages

19 years and older

Sex

Anyone

Healthy volunteers

Not accepted

This study is not open to healthy volunteers. The entry requirements below say who it is open to.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

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

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Conditions

The condition(s) this trial relates to.

idiopathic pulmonary fibrosis interstitial lung disease

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

Idiopathic pulmonary fibrosis Interstitial lung disease

Contacts and locations

Locations

  • Chung-Ang University Hospital

    Seoul, South Korea

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