Artificial Intelligence (AI) Detection of Incidental Interstitial Opacity on Chest Radiography
5. juli 2026 opdateret af: Kyoungmin Moon, Chung-Ang University Hospital
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.
Studieoversigt
Status
Afsluttet
Betingelser
Intervention / Behandling
Undersøgelsestype
Observationel
Tilmelding (Faktiske)
1293
Kontakter og lokationer
Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.
Studiesteder
-
-
Seoul
-
Seoul, Seoul, Sydkorea, 06973
- Chung-Ang University Hospital
-
-
Deltagelseskriterier
Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.
Berettigelseskriterier
Aldre berettiget til at studere
- Voksen
- Ældre voksen
Tager imod sunde frivillige
Ja
Prøveudtagningsmetode
Ikke-sandsynlighedsprøve
Studiebefolkning
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.
Beskrivelse
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
Studieplan
Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
Kohorter og interventioner
Gruppe / kohorte |
Intervention / Behandling |
|---|---|
|
AI interstitial opacity-positive
Patients whose index chest radiograph was flagged as interstitial opacity-positive by the AI software.
|
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.
|
Hvad måler undersøgelsen?
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
|
Positive predictive value (PPV) of AI-detected interstitial opacity
Tidsramme: 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.
|
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
|
Sekundære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
|
Comparison of AI finding scores between true-positive and false-positive cases
Tidsramme: 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).
|
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
|
Samarbejdspartnere og efterforskere
Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.
Sponsor
Samarbejdspartnere
Datoer for undersøgelser
Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.
Studer store datoer
Studiestart (Faktiske)
1. februar 2022
Primær færdiggørelse (Faktiske)
31. december 2024
Studieafslutning (Faktiske)
31. december 2024
Datoer for studieregistrering
Først indsendt
25. juni 2026
Først indsendt, der opfyldte QC-kriterier
5. juli 2026
Først opslået (Faktiske)
7. juli 2026
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
7. juli 2026
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
5. juli 2026
Sidst verificeret
1. juli 2026
Mere information
Begreber relateret til denne undersøgelse
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
- 2411-003-19548
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