DeepMedFake-Assisted Detection of Synthetic and Discordant Medical Images
12 septembre 2026 mis à jour par: 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.
Aperçu de l'étude
Statut
Pas encore de recrutement
Les conditions
Intervention / Traitement
Type d'étude
Interventionnel
Inscription (Estimé)
64
Phase
- N'est pas applicable
Contacts et emplacements
Cette section fournit les coordonnées de ceux qui mènent l'étude et des informations sur le lieu où cette étude est menée.
Coordonnées de l'étude
- Nom: Han Lv
- Numéro de téléphone: +8613901073227
- E-mail: chrislvhan@126.com
Lieux d'étude
-
-
Beijing Municipality
-
Beijing, Beijing Municipality, Chine, 100050
- Beijing Friendship hospital, Capital Medical University
-
Contact:
- Han Lv, Professor
- Numéro de téléphone: +8613901073227
- E-mail: chrislvhan@126.com
-
-
Critères de participation
Les chercheurs recherchent des personnes qui correspondent à une certaine description, appelée critères d'éligibilité. Certains exemples de ces critères sont l'état de santé général d'une personne ou des traitements antérieurs.
Critère d'éligibilité
Âges éligibles pour étudier
- Adulte
- Adulte plus âgé
Accepte les volontaires sains
Oui
La description
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 d'étude
Cette section fournit des détails sur le plan d'étude, y compris la façon dont l'étude est conçue et ce que l'étude mesure.
Comment l'étude est-elle conçue ?
Détails de conception
- Objectif principal: Autre
- Répartition: Randomisé
- Modèle interventionnel: Affectation croisée
- Masquage: Seul
Armes et Interventions
Groupe de participants / Bras |
Intervention / Traitement |
|---|---|
|
Expérimental: 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.
|
|
Expérimental: 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.
|
|
Expérimental: 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.
|
|
Expérimental: 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.
|
Que mesure l'étude ?
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
|
Reader Performance With and Without DeepMedFake Assistance: Sensitivity of the Final Verification Decision in the Hospital Clinical Cohort
Délai: 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
Délai: 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
|
Mesures de résultats secondaires
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
|
Reader Performance With and Without DeepMedFake Assistance: Specificity of the Final Verification Decision in the Hospital Clinical Cohort
Délai: 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
Délai: 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
Délai: 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
Délai: 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
Délai: 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
Délai: 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
Délai: 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
Délai: 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
Délai: 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
|
Collaborateurs et enquêteurs
C'est ici que vous trouverez les personnes et les organisations impliquées dans cette étude.
Parrainer
Dates d'enregistrement des études
Ces dates suivent la progression des dossiers d'étude et des soumissions de résultats sommaires à ClinicalTrials.gov. Les dossiers d'étude et les résultats rapportés sont examinés par la Bibliothèque nationale de médecine (NLM) pour s'assurer qu'ils répondent à des normes de contrôle de qualité spécifiques avant d'être publiés sur le site Web public.
Dates principales de l'étude
Début de l'étude (Estimé)
20 septembre 2026
Achèvement primaire (Estimé)
20 octobre 2026
Achèvement de l'étude (Estimé)
31 octobre 2026
Dates d'inscription aux études
Première soumission
12 septembre 2026
Première soumission répondant aux critères de contrôle qualité
12 septembre 2026
Première publication (Réel)
16 septembre 2026
Mises à jour des dossiers d'étude
Dernière mise à jour publiée (Réel)
16 septembre 2026
Dernière mise à jour soumise répondant aux critères de contrôle qualité
12 septembre 2026
Dernière vérification
1 septembre 2026
Plus d'information
Termes liés à cette étude
Mots clés
Autres numéros d'identification d'étude
- 2026-P2-308-01
Plan pour les données individuelles des participants (IPD)
Prévoyez-vous de partager les données individuelles des participants (DPI) ?
NON
Informations sur les médicaments et les dispositifs, documents d'étude
Étudie un produit pharmaceutique réglementé par la FDA américaine
Non
Étudie un produit d'appareil réglementé par la FDA américaine
Non
Ces informations ont été extraites directement du site Web clinicaltrials.gov sans aucune modification. Si vous avez des demandes de modification, de suppression ou de mise à jour des détails de votre étude, veuillez contacter register@clinicaltrials.gov. Dès qu'un changement est mis en œuvre sur clinicaltrials.gov, il sera également mis à jour automatiquement sur notre site Web .