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

NCT07686562 | Completed

• 1 other identifier: 2411-003-19548
• Study Type: observational
• Target: 1,293
• Locations: 1 country
• Sites: 1

Brief 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 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

Outcome Measures

Primary Outcomes (1)

• 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.

  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

  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

Study Arms (1)

AI interstitial opacity-positive

Patients whose index chest radiograph was flagged as interstitial opacity-positive by the AI software.

Device: VUNO Med®-Chest X-ray™

Interventions

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.

AI interstitial opacity-positive

Eligibility Criteria

• Age: 19 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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™

You may not qualify if:

• 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

Sponsors & Collaborators

• VUNO Inc.: collaborator
• Chung-Ang University Hospital: lead

Study Sites (1)

Chung-Ang University Hospital

Seoul, Seoul, 06973, South Korea

Location

MeSH Terms

Conditions

Lung Diseases, Interstitial; Idiopathic Pulmonary Fibrosis

Condition Hierarchy (Ancestors)

Lung Diseases; Respiratory Tract Diseases; Pulmonary Fibrosis

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor of Pulmonary and Allergy Medicine

Study Record Dates

• First Submitted: June 25, 2026
• First Posted: July 7, 2026
• Study Start: February 1, 2022
• Primary Completion: December 31, 2024
• Study Completion: December 31, 2024
• Last Updated: July 7, 2026

Record last verified: 2026-07

Locations

KR (1)