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Completed

NCT Number: NCT07686562

Artificial Intelligence (AI) Detection of Incidental Interstitial Opacity on Chest Radiography

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.

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Key information

Conditions

Lung Disease, Interstitial Chest X-ray for Clinical Evaluation Idiopathic Pulmonary Fibrosis Idiopathic Pulmonary Fibrosis (IPF) Incidental Findings Interstitial Lung Disease (ILD) Lung Diseases Lung Diseases, Interstitial Pulmonary Fibrosis Respiratory Tract Diseases

Age range

19 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Chung-Ang University Hospital

Seoul, 06973, South Korea

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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™

Exclusion criteria

  • 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

Treatment and study plan

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.

Primary outcomes

  1. 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.

Secondary outcomes

  1. 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

    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).

Sponsors and collaborators

Lead sponsor

Chung-Ang University Hospital

Other

Collaborators

  • VUNO Inc.

Registry information

Official study title

Evaluating the Real-World Performance of Artificial Intelligence (AI)-Based Detection for Interstitial Lung Disease in Chest X-Ray Images

Important dates

Study start
2022
Primary completion
2024
Study completion
2024
First posted
Jul 7, 2026
Registry last updated
Jul 7, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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