Ta strona została przetłumaczona automatycznie i dokładność tłumaczenia nie jest gwarantowana. Proszę odnieść się do angielska wersja za tekst źródłowy.

DeepMedFake-Assisted Detection of Synthetic and Discordant Medical Images

12 września 2026 zaktualizowane przez: 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.

Przegląd badań

Status

Jeszcze nie rekrutacja

Warunki

Interwencja / Leczenie

Typ studiów

Interwencyjne

Zapisy (Szacowany)

64

Faza

  • Nie dotyczy

Kontakty i lokalizacje

Ta sekcja zawiera dane kontaktowe osób prowadzących badanie oraz informacje o tym, gdzie badanie jest przeprowadzane.

Kontakt w sprawie studiów

Lokalizacje studiów

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

Kryteria uczestnictwa

Badacze szukają osób, które pasują do określonego opisu, zwanego kryteriami kwalifikacyjnymi. Niektóre przykłady tych kryteriów to ogólny stan zdrowia danej osoby lub wcześniejsze leczenie.

Kryteria kwalifikacji

Wiek uprawniający do nauki

  • Dorosły
  • Starszy dorosły

Akceptuje zdrowych ochotników

Tak

Opis

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.

Plan studiów

Ta sekcja zawiera szczegółowe informacje na temat planu badania, w tym sposób zaprojektowania badania i jego pomiary.

Jak projektuje się badanie?

Szczegóły projektu

  • Główny cel: Inny
  • Przydział: Randomizowane
  • Model interwencyjny: Zadanie krzyżowe
  • Maskowanie: Pojedynczy

Broń i interwencje

Grupa uczestników / Arm
Interwencja / Leczenie
Eksperymentalny: 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.
Eksperymentalny: 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.
Eksperymentalny: 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.
Eksperymentalny: 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.

Co mierzy badanie?

Podstawowe miary wyniku

Miara wyniku
Opis środka
Ramy czasowe
Reader Performance With and Without DeepMedFake Assistance: Sensitivity of the Final Verification Decision in the Hospital Clinical Cohort
Ramy czasowe: 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
Ramy czasowe: 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

Miary wyników drugorzędnych

Miara wyniku
Opis środka
Ramy czasowe
Reader Performance With and Without DeepMedFake Assistance: Specificity of the Final Verification Decision in the Hospital Clinical Cohort
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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

Współpracownicy i badacze

Tutaj znajdziesz osoby i organizacje zaangażowane w to badanie.

Sponsor

Daty zapisu na studia

Daty te śledzą postęp w przesyłaniu rekordów badań i podsumowań wyników do ClinicalTrials.gov. Zapisy badań i zgłoszone wyniki są przeglądane przez National Library of Medicine (NLM), aby upewnić się, że spełniają określone standardy kontroli jakości, zanim zostaną opublikowane na publicznej stronie internetowej.

Główne daty studiów

Rozpoczęcie studiów (Szacowany)

20 września 2026

Zakończenie podstawowe (Szacowany)

20 października 2026

Ukończenie studiów (Szacowany)

31 października 2026

Daty rejestracji na studia

Pierwszy przesłany

12 września 2026

Pierwszy przesłany, który spełnia kryteria kontroli jakości

12 września 2026

Pierwszy wysłany (Rzeczywisty)

16 września 2026

Aktualizacje rekordów badań

Ostatnia wysłana aktualizacja (Rzeczywisty)

16 września 2026

Ostatnia przesłana aktualizacja, która spełniała kryteria kontroli jakości

12 września 2026

Ostatnia weryfikacja

1 września 2026

Więcej informacji

Terminy związane z tym badaniem

Inne numery identyfikacyjne badania

  • 2026-P2-308-01

Plan dla danych uczestnika indywidualnego (IPD)

Planujesz udostępniać dane poszczególnych uczestników (IPD)?

NIE

Informacje o lekach i urządzeniach, dokumenty badawcze

Bada produkt leczniczy regulowany przez amerykańską FDA

Nie

Bada produkt urządzenia regulowany przez amerykańską FDA

Nie

Te informacje zostały pobrane bezpośrednio ze strony internetowej clinicaltrials.gov bez żadnych zmian. Jeśli chcesz zmienić, usunąć lub zaktualizować dane swojego badania, skontaktuj się z register@clinicaltrials.gov. Gdy tylko zmiana zostanie wprowadzona na stronie clinicaltrials.gov, zostanie ona automatycznie zaktualizowana również na naszej stronie internetowej .