AI-Assisted Chest CT Interpretation Across the Lung Cancer Care Continuum
Development of a Chest CT-Based Artificial Intelligence Model Across the Lung Cancer Care Continuum and a Prospective Randomized Crossover Reader Study of Its Clinical Decision-Support Performance
This investigator-initiated, single-center study consists of a retrospective artificial intelligence model-development stage and a prospective physician reader-study stage. Deidentified chest CT examinations acquired during routine clinical care between January 1, 2021, and December 31, 2024, will be used to develop, validate, and lock artificial intelligence models for lung cancer-related imaging tasks.
In the prospective stage, approximately 12 to 15 physicians with experience in chest CT interpretation will complete two reading sessions in randomized order: unaided interpretation and AI-assisted interpretation. The sessions will be separated by a washout period of at least 4 weeks. The primary objective is to compare diagnostic performance between AI-assisted and unaided interpretation. Secondary objectives include reading time, diagnostic confidence, inter-reader agreement, and errors related to incorrect AI suggestions.
All readings will be performed in an offline research environment. AI outputs will not be used for patient care, and the study will not add imaging examinations, treatment, or follow-up for patients.
Studienübersicht
Status
Bedingungen
Intervention / Behandlung
Studientyp
Einschreibung (Geschätzt)
Phase
- Unzutreffend
Kontakte und Standorte
Studienkontakt
- Name: Lian Yang, PhD
- Telefonnummer: +86 18986273791
- E-Mail: yanglian@hust.edu.cn
Studienorte
-
-
Hubei
-
Wuhan, Hubei, China, 430022
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
-
Kontakt:
- Lian Yang, PhD
- Telefonnummer: +86 18986273791
- E-Mail: yanglian@hust.edu.cn
-
-
Teilnahmekriterien
Zulassungskriterien
Studienberechtigtes Alter
Akzeptiert gesunde Freiwillige
Studienberechtigte Geschlechter
Beschreibung
Inclusion Criteria:
- Physicians with education or training relevant to medical imaging
- Experience in interpreting chest CT examinations
- Ability to complete the required study training and both reading sessions
- Willingness to provide written informed consent and comply with study procedures
Exclusion Criteria:
- Failure to complete the required study training
- Inability or unwillingness to complete both reading sessions as required
- Any protocol deviation likely to compromise the validity of the reader-study data
Studienplan
Wie ist die Studie aufgebaut?
Designdetails
- Hauptzweck: Diagnose
- Zuteilung: Zufällig
- Interventionsmodell: Crossover-Aufgabe
- Maskierung: Keine (Offenes Etikett)
Waffen und Interventionen
Teilnehmergruppe / Arm |
Intervention / Behandlung |
|---|---|
|
Experimental: Sequence AB: Unaided Then AI-Assisted Interpretation
Participants first complete an unaided chest CT interpretation session. After a washout period of at least 4 weeks, they complete an AI-assisted interpretation session. |
A locked research-use-only artificial intelligence system analyzes deidentified chest CT images and provides decision-support outputs to participating physicians. The system is evaluated only in an offline reader-study environment and is not used for actual patient care. |
|
Experimental: Sequence BA: AI-Assisted Then Unaided Interpretation
Participants first complete an AI-assisted chest CT interpretation session. After a washout period of at least 4 weeks, they complete an unaided interpretation session. |
A locked research-use-only artificial intelligence system analyzes deidentified chest CT images and provides decision-support outputs to participating physicians. The system is evaluated only in an offline reader-study environment and is not used for actual patient care. |
Was misst die Studie?
Primäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
|
Difference in Diagnostic Accuracy Between AI-Assisted and Unaided Chest CT Interpretation
Zeitfenster: At completion of the second reading session, after a washout period of at least 4 weeks
|
Diagnostic accuracy will be calculated as the proportion of case-level interpretations that agree with the prespecified reference standard based on pathology or clinical follow-up. The paired difference in diagnostic accuracy between AI-assisted and unaided interpretation will be estimated across participating physicians and cases using a multi-reader multi-case analysis and reported with a 95% confidence interval. Higher accuracy indicates better diagnostic performance. |
At completion of the second reading session, after a washout period of at least 4 weeks
|
Mitarbeiter und Ermittler
Studienaufzeichnungsdaten
Haupttermine studieren
Studienbeginn (Geschätzt)
Primärer Abschluss (Geschätzt)
Studienabschluss (Geschätzt)
Studienanmeldedaten
Zuerst eingereicht
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
Zuerst gepostet (Tatsächlich)
Studienaufzeichnungsaktualisierungen
Letztes Update gepostet (Tatsächlich)
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
Zuletzt verifiziert
Mehr Informationen
Begriffe im Zusammenhang mit dieser Studie
Schlüsselwörter
Zusätzliche relevante MeSH-Bedingungen
Andere Studien-ID-Nummern
- Radio-20260816
Plan für individuelle Teilnehmerdaten (IPD)
Planen Sie, individuelle Teilnehmerdaten (IPD) zu teilen?
Arzneimittel- und Geräteinformationen, Studienunterlagen
Studiert ein von der US-amerikanischen FDA reguliertes Arzneimittelprodukt
Studiert ein von der US-amerikanischen FDA reguliertes Geräteprodukt
Diese Informationen wurden ohne Änderungen direkt von der Website clinicaltrials.gov abgerufen. Wenn Sie Ihre Studiendaten ändern, entfernen oder aktualisieren möchten, wenden Sie sich bitte an register@clinicaltrials.gov. Sobald eine Änderung auf clinicaltrials.gov implementiert wird, wird diese automatisch auch auf unserer Website aktualisiert .