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DeepMedFake-Assisted Detection of Synthetic and Discordant Medical Images

12. September 2026 aktualisiert von: Lv, Han, Beijing Friendship Hospital

Evaluation of the Clinical Performance and Multi-Setting Application of an AI-Based Medical Image Verification System

This prospective randomized controlled trial will evaluate whether DeepMedFake improves the identification of medical images that require further verification. DeepMedFake is an artificial intelligence-based medical image analysis system that provides an image-authenticity risk assessment, a patient-image consistency risk assessment and a recommendation regarding further verification. Hospital physicians and healthcare audit professionals will participate in a randomized two-period crossover design. Each participant will assess one case set with DeepMedFake assistance and the other without AI assistance. The primary outcome in each cohort is the sensitivity of the final verification decision, evaluated separately in the hospital clinical and healthcare audit cohorts.

Studienübersicht

Status

Noch keine Rekrutierung

Bedingungen

Intervention / Behandlung

Studientyp

Interventionell

Einschreibung (Geschätzt)

64

Phase

  • Unzutreffend

Kontakte und Standorte

Dieser Abschnitt enthält die Kontaktdaten derjenigen, die die Studie durchführen, und Informationen darüber, wo diese Studie durchgeführt wird.

Studienkontakt

Studienorte

    • Beijing Municipality
      • Beijing, Beijing Municipality, China, 100050
        • Beijing Friendship hospital, Capital Medical University
        • Kontakt:

Teilnahmekriterien

Forscher suchen nach Personen, die einer bestimmten Beschreibung entsprechen, die als Auswahlkriterien bezeichnet werden. Einige Beispiele für diese Kriterien sind der allgemeine Gesundheitszustand einer Person oder frühere Behandlungen.

Zulassungskriterien

Studienberechtigtes Alter

  • Erwachsene
  • Älterer Erwachsener

Akzeptiert gesunde Freiwillige

Ja

Beschreibung

Inclusion Criteria:

  • Aged 18 years or older.
  • Able to complete reading periods and all required electronic study procedures.
  • Completed the standardized study training and practice cases.
  • Provided written informed consent before participation.

    (1)Hospital clinical cohort participants must also meet the following criteria:

  • Hold a valid physician qualification.
  • Currently practice in ophthalmology, radiology, ultrasound medicine, or a clinical specialty in which imaging of the neurological or cerebrovascular, cardiovascular, thoracic, abdominal, or musculoskeletal domain is routinely reviewed.
  • Have direct professional experience reviewing all imaging modalities and clinical imaging domains assigned to them in the study.
  • Routinely use the assigned medical images for image interpretation, image verification or clinical decision-making.

    (2)Healthcare audit cohort participants must also meet the following criteria:

  • Currently work in healthcare audit, medical reimbursement review, medical-cost review or medical-material verification.
  • Have experience reviewing medical imaging materials as part of their routine work.
  • Have knowledge required to interpret the medical images and patient information presented in the study and to determine whether further verification is required.

Exclusion Criteria:

  • Direct involvement in development of the locked DeepMedFake model, determination of model weights or selection of decision thresholds.
  • Direct involvement in selection or construction of the formal case library or adjudication of the reference standard.
  • Direct involvement in generation or implementation of the reader randomization sequence.
  • Previous participation as a reader in the pilot study.
  • Previous access to any formal study case, case-construction record or reference-standard label.
  • Access to undisclosed study information that could permit advance identification of case type or image-generation method.
  • A financial, professional or other conflict of interest considered likely to compromise independent case assessment.

Studienplan

Dieser Abschnitt enthält Einzelheiten zum Studienplan, einschließlich des Studiendesigns und der Messung der Studieninhalte.

Wie ist die Studie aufgebaut?

Designdetails

  • Hauptzweck: Sonstiges
  • Zuteilung: Zufällig
  • Interventionsmodell: Crossover-Aufgabe
  • Maskierung: Single

Waffen und Interventionen

Teilnehmergruppe / Arm
Intervention / Behandlung
Experimental: Crossover Sequence A
Participants undergo unassisted review of case set S1 during Period 1, followed by DeepMedFake-assisted review of case set S2 during Period 2.
DeepMedFake is an artificial intelligence-based medical image analysis system designed to support the detection of synthetic medical images and authentic medical images paired with discordant patient information. It provides an image-authenticity risk assessment, a patient-image consistency risk assessment and a recommendation regarding further verification. In this study, DeepMedFake is used as a decision-support tool to assist participants in determining whether the image requires further verification before downstream clinical or healthcare audit use. Final judgments and verification decisions are made by the participants.
Experimental: Crossover Sequence B
Participants undergo DeepMedFake-assisted review of case set S1 during Period 1, followed by unassisted review of case set S2 during Period 2.
DeepMedFake is an artificial intelligence-based medical image analysis system designed to support the detection of synthetic medical images and authentic medical images paired with discordant patient information. It provides an image-authenticity risk assessment, a patient-image consistency risk assessment and a recommendation regarding further verification. In this study, DeepMedFake is used as a decision-support tool to assist participants in determining whether the image requires further verification before downstream clinical or healthcare audit use. Final judgments and verification decisions are made by the participants.
Experimental: Crossover Sequence C
Participants undergo unassisted review of case set S2 during Period 1, followed by DeepMedFake-assisted review of case set S1 during Period 2.
DeepMedFake is an artificial intelligence-based medical image analysis system designed to support the detection of synthetic medical images and authentic medical images paired with discordant patient information. It provides an image-authenticity risk assessment, a patient-image consistency risk assessment and a recommendation regarding further verification. In this study, DeepMedFake is used as a decision-support tool to assist participants in determining whether the image requires further verification before downstream clinical or healthcare audit use. Final judgments and verification decisions are made by the participants.
Experimental: Crossover Sequence D
Participants undergo DeepMedFake-assisted review of case set S2 during Period 1, followed by unassisted review of case set S1 during Period 2.
DeepMedFake is an artificial intelligence-based medical image analysis system designed to support the detection of synthetic medical images and authentic medical images paired with discordant patient information. It provides an image-authenticity risk assessment, a patient-image consistency risk assessment and a recommendation regarding further verification. In this study, DeepMedFake is used as a decision-support tool to assist participants in determining whether the image requires further verification before downstream clinical or healthcare audit use. Final judgments and verification decisions are made by the participants.

Was misst die Studie?

Primäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Reader Performance With and Without DeepMedFake Assistance: Sensitivity of the Final Verification Decision in the Hospital Clinical Cohort
Zeitfenster: During both reading periods, up to 4 weeks after randomization
The percentage of reference-standard abnormal cases assigned to further verification by hospital physicians will be estimated under DeepMedFake-assisted and unassisted review. The effect measure is the absolute percentage-point difference between review conditions.
During both reading periods, up to 4 weeks after randomization
Reader Performance With and Without DeepMedFake Assistance: Sensitivity of the Final Verification Decision in the Healthcare Audit Cohort
Zeitfenster: During both reading periods, up to 4 weeks after randomization
The percentage of reference-standard abnormal cases assigned to further verification by healthcare audit professionals will be estimated under DeepMedFake-assisted and unassisted review. The effect measure is the absolute percentage-point difference between review conditions.
During both reading periods, up to 4 weeks after randomization

Sekundäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Reader Performance With and Without DeepMedFake Assistance: Specificity of the Final Verification Decision in the Hospital Clinical Cohort
Zeitfenster: During both reading periods, up to 4 weeks after randomization
The percentage of authentic, information-matched cases assigned to no further verification by hospital physicians will be estimated under each review condition and compared between conditions.
During both reading periods, up to 4 weeks after randomization
Reader Performance With and Without DeepMedFake Assistance: Specificity of the Final Verification Decision in the Healthcare Audit Cohort
Zeitfenster: During both reading periods, up to 4 weeks after randomization
The percentage of authentic, information-matched cases assigned to no further verification by healthcare audit professionals will be estimated under each review condition and compared between conditions.
During both reading periods, up to 4 weeks after randomization
Sensitivity of the Final Verification Decision for Synthetic Medical Image Cases
Zeitfenster: During both reading periods, up to 4 weeks after randomization
The percentage of reference-standard synthetic-image cases assigned to further verification will be estimated under each review condition. DeepMedFake-assisted and unassisted review will be compared separately within the hospital clinical and healthcare audit cohorts.
During both reading periods, up to 4 weeks after randomization
Sensitivity of the Final Verification Decision in Discordant Cases
Zeitfenster: During both reading periods, up to 4 weeks after randomization
The percentage of authentic images paired with discordant patient information that are assigned to further verification will be estimated under each review condition. DeepMedFake-assisted and unassisted review will be compared separately within the hospital clinical and healthcare audit cohorts.
During both reading periods, up to 4 weeks after randomization
Sensitivity of the Image-Authenticity Judgment
Zeitfenster: During both reading periods, up to 4 weeks after randomization
The percentage of reference-standard synthetic-image cases classified as suspected synthetic images will be estimated under each review condition and compared separately within each reader cohort.
During both reading periods, up to 4 weeks after randomization
Specificity of the Image-Authenticity Judgment
Zeitfenster: During both reading periods, up to 4 weeks after randomization
The percentage of reference-standard authentic-image cases classified as not suspected to be synthetic will be estimated under each review condition and compared separately within each reader cohort.
During both reading periods, up to 4 weeks after randomization
Sensitivity of the Patient-Image Consistency Judgment
Zeitfenster: During both reading periods, up to 4 weeks after randomization
Among authentic images, the percentage of discordant cases classified as discordant with the displayed patient information will be estimated under each review condition and compared separately within each reader cohort. Synthetic-image cases will be excluded from this analysis.
During both reading periods, up to 4 weeks after randomization
Specificity of the Patient-Image Consistency Judgment
Zeitfenster: During both reading periods, up to 4 weeks after randomization
Among authentic images, the percentage of information-matched cases classified as matching the displayed patient information will be estimated under each review condition and compared separately within each reader cohort. Synthetic-image cases will be excluded from this analysis.
During both reading periods, up to 4 weeks after randomization
Case-Level Workflow Time
Zeitfenster: During both reading periods, up to 4 weeks after randomization
Elapsed time in seconds from successful case loading to submission of the final required case-level response, including selection of a proposed first verification pathway when required. Recorded pauses will be excluded. Workflow time will be compared between review conditions separately within each reader cohort.
During both reading periods, up to 4 weeks after randomization

Mitarbeiter und Ermittler

Hier finden Sie Personen und Organisationen, die an dieser Studie beteiligt sind.

Sponsor

Studienaufzeichnungsdaten

Diese Daten verfolgen den Fortschritt der Übermittlung von Studienaufzeichnungen und zusammenfassenden Ergebnissen an ClinicalTrials.gov. Studienaufzeichnungen und gemeldete Ergebnisse werden von der National Library of Medicine (NLM) überprüft, um sicherzustellen, dass sie bestimmten Qualitätskontrollstandards entsprechen, bevor sie auf der öffentlichen Website veröffentlicht werden.

Haupttermine studieren

Studienbeginn (Geschätzt)

20. September 2026

Primärer Abschluss (Geschätzt)

20. Oktober 2026

Studienabschluss (Geschätzt)

31. Oktober 2026

Studienanmeldedaten

Zuerst eingereicht

12. September 2026

Zuerst eingereicht, das die QC-Kriterien erfüllt hat

12. September 2026

Zuerst gepostet (Tatsächlich)

16. September 2026

Studienaufzeichnungsaktualisierungen

Letztes Update gepostet (Tatsächlich)

16. September 2026

Letztes eingereichtes Update, das die QC-Kriterien erfüllt

12. September 2026

Zuletzt verifiziert

1. September 2026

Mehr Informationen

Begriffe im Zusammenhang mit dieser Studie

Andere Studien-ID-Nummern

  • 2026-P2-308-01

Plan für individuelle Teilnehmerdaten (IPD)

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Studiert ein von der US-amerikanischen FDA reguliertes Arzneimittelprodukt

Nein

Studiert ein von der US-amerikanischen FDA reguliertes Geräteprodukt

Nein

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