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

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.

Study Overview

• Status: Completed
• Conditions: Lung Disease, Interstitial; Interstitial Lung Disease (ILD); Idiopathic Pulmonary Fibrosis (IPF); Incidental Findings; Chest X-ray for Clinical Evaluation
• Intervention / Treatment: Device: VUNO Med®-Chest X-ray™

• Study Type: Observational
• Enrollment (Actual): 1293

Contacts and Locations

Study Locations

• South Korea
  • Seoul
    • Seoul, Seoul, South Korea, 06973
      • Chung-Ang University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• 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.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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™; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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).	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

Collaborators and Investigators

• Sponsor: Chung-Ang University Hospital
• Collaborators: VUNO Inc.

Study record dates

Study Major Dates

• Study Start (Actual): February 1, 2022
• Primary Completion (Actual): December 31, 2024
• Study Completion (Actual): December 31, 2024

Study Registration Dates

• First Submitted: June 25, 2026
• First Submitted That Met QC Criteria: July 5, 2026
• First Posted (Actual): July 7, 2026

Study Record Updates

• Last Update Posted (Actual): July 7, 2026
• Last Update Submitted That Met QC Criteria: July 5, 2026
• Last Verified: July 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Lung Diseases; Pulmonary Fibrosis; Idiopathic Pulmonary Fibrosis; Lung Diseases, Interstitial
• Other Study ID Numbers: 2411-003-19548

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No