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DeepMedFake-Assisted Detection of Synthetic and Discordant Medical Images

12. september 2026 oppdatert av: 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.

Studieoversikt

Status

Har ikke rekruttert ennå

Forhold

Intervensjon / Behandling

Studietype

Intervensjonell

Registrering (Antatt)

64

Fase

  • Ikke aktuelt

Kontakter og plasseringer

Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.

Studiekontakt

Studiesteder

  • Kina
    • Beijing Municipality
      • Beijing, Beijing Municipality, Kina, 100050
        • Beijing Friendship hospital, Capital Medical University
        • Ta kontakt med:

Deltakelseskriterier

Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.

Kvalifikasjonskriterier

Alder som er kvalifisert for studier

  • Voksen
  • Eldre voksen

Tar imot friske frivillige

Ja

Beskrivelse

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.

Studieplan

Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.

Hvordan er studiet utformet?

Designdetaljer

  • Primært formål: Annen
  • Tildeling: Randomisert
  • Intervensjonsmodell: Crossover-oppdrag
  • Masking: Enkelt

Våpen og intervensjoner

Deltakergruppe / Arm
Intervensjon / Behandling
Eksperimentell: 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.
Enhet: DeepMedFake
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.
Eksperimentell: 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.
Enhet: DeepMedFake
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.
Eksperimentell: 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.
Enhet: DeepMedFake
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.
Eksperimentell: 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.
Enhet: DeepMedFake
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.

Hva måler studien?

Primære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
Reader Performance With and Without DeepMedFake Assistance: Sensitivity of the Final Verification Decision in the Hospital Clinical Cohort
Tidsramme: 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
Tidsramme: 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

Sekundære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
Reader Performance With and Without DeepMedFake Assistance: Specificity of the Final Verification Decision in the Hospital Clinical Cohort
Tidsramme: 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
Tidsramme: 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
Tidsramme: 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
Tidsramme: 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
Tidsramme: 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
Tidsramme: 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
Tidsramme: 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
Tidsramme: 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
Tidsramme: 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

Samarbeidspartnere og etterforskere

Det er her du vil finne personer og organisasjoner som er involvert i denne studien.

Sponsor

Beijing Friendship Hospital

Studierekorddatoer

Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.

Studer hoveddatoer

Studiestart (Antatt)

20. september 2026

Primær fullføring (Antatt)

20. oktober 2026

Studiet fullført (Antatt)

31. oktober 2026

Datoer for studieregistrering

Først innsendt

12. september 2026

Først innsendt som oppfylte QC-kriteriene

12. september 2026

Først lagt ut (Faktiske)

16. september 2026

Oppdateringer av studieposter

Sist oppdatering lagt ut (Faktiske)

16. september 2026

Siste oppdatering sendt inn som oppfylte QC-kriteriene

12. september 2026

Sist bekreftet

1. september 2026

Mer informasjon

Begreper knyttet til denne studien

Nøkkelord

Andre studie-ID-numre

  • 2026-P2-308-01

Plan for individuelle deltakerdata (IPD)

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NEI

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Studerer et amerikansk FDA-regulert medikamentprodukt

Nei

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Nei

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