DeepMedFake-Assisted Detection of Synthetic and Discordant Medical Images
12 september 2026 uppdaterad 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.
Studieöversikt
Status
Har inte rekryterat ännu
Betingelser
Intervention / Behandling
Studietyp
Interventionell
Inskrivning (Beräknad)
64
Fas
- Inte tillämpbar
Kontakter och platser
Det här avsnittet innehåller kontaktuppgifter för dem som genomför studien och information om var denna studie genomförs.
Studiekontakt
- Namn: Han Lv
- Telefonnummer: +8613901073227
- E-post: chrislvhan@126.com
Studieorter
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Beijing Municipality
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Beijing, Beijing Municipality, Kina, 100050
- Beijing Friendship hospital, Capital Medical University
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Kontakt:
- Han Lv, Professor
- Telefonnummer: +8613901073227
- E-post: chrislvhan@126.com
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-
Deltagandekriterier
Forskare letar efter personer som passar en viss beskrivning, så kallade behörighetskriterier. Några exempel på dessa kriterier är en persons allmänna hälsotillstånd eller tidigare behandlingar.
Urvalskriterier
Åldrar som är berättigade till studier
- Vuxen
- Äldre vuxen
Tar emot friska volontärer
Ja
Beskrivning
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
Det här avsnittet ger detaljer om studieplanen, inklusive hur studien är utformad och vad studien mäter.
Hur är studien utformad?
Designdetaljer
- Primärt syfte: Övrig
- Tilldelning: Randomiserad
- Interventionsmodell: Crossover tilldelning
- Maskning: Enda
Vapen och interventioner
Deltagargrupp / Arm |
Intervention / Behandling |
|---|---|
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Experimentell: 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.
|
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Experimentell: 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.
|
|
Experimentell: 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.
|
|
Experimentell: 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.
|
Vad mäter studien?
Primära resultatmått
Resultatmått |
Åtgärdsbeskrivning |
Tidsram |
|---|---|---|
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Reader Performance With and Without DeepMedFake Assistance: Sensitivity of the Final Verification Decision in the Hospital Clinical Cohort
Tidsram: During both reading periods, up to 4 weeks after randomization
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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.
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During both reading periods, up to 4 weeks after randomization
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Reader Performance With and Without DeepMedFake Assistance: Sensitivity of the Final Verification Decision in the Healthcare Audit Cohort
Tidsram: 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
|
Sekundära resultatmått
Resultatmått |
Åtgärdsbeskrivning |
Tidsram |
|---|---|---|
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Reader Performance With and Without DeepMedFake Assistance: Specificity of the Final Verification Decision in the Hospital Clinical Cohort
Tidsram: During both reading periods, up to 4 weeks after randomization
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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.
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During both reading periods, up to 4 weeks after randomization
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Reader Performance With and Without DeepMedFake Assistance: Specificity of the Final Verification Decision in the Healthcare Audit Cohort
Tidsram: During both reading periods, up to 4 weeks after randomization
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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.
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During both reading periods, up to 4 weeks after randomization
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Sensitivity of the Final Verification Decision for Synthetic Medical Image Cases
Tidsram: During both reading periods, up to 4 weeks after randomization
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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.
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During both reading periods, up to 4 weeks after randomization
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Sensitivity of the Final Verification Decision in Discordant Cases
Tidsram: During both reading periods, up to 4 weeks after randomization
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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.
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During both reading periods, up to 4 weeks after randomization
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Sensitivity of the Image-Authenticity Judgment
Tidsram: During both reading periods, up to 4 weeks after randomization
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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.
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During both reading periods, up to 4 weeks after randomization
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Specificity of the Image-Authenticity Judgment
Tidsram: During both reading periods, up to 4 weeks after randomization
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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.
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During both reading periods, up to 4 weeks after randomization
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Sensitivity of the Patient-Image Consistency Judgment
Tidsram: During both reading periods, up to 4 weeks after randomization
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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.
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During both reading periods, up to 4 weeks after randomization
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Specificity of the Patient-Image Consistency Judgment
Tidsram: During both reading periods, up to 4 weeks after randomization
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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.
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During both reading periods, up to 4 weeks after randomization
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Case-Level Workflow Time
Tidsram: During both reading periods, up to 4 weeks after randomization
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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.
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During both reading periods, up to 4 weeks after randomization
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Samarbetspartners och utredare
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Sponsor
Studieavstämningsdatum
Dessa datum spårar framstegen för inlämningar av studieposter och sammanfattande resultat till ClinicalTrials.gov. Studieposter och rapporterade resultat granskas av National Library of Medicine (NLM) för att säkerställa att de uppfyller specifika kvalitetskontrollstandarder innan de publiceras på den offentliga webbplatsen.
Studera stora datum
Studiestart (Beräknad)
20 september 2026
Primärt slutförande (Beräknad)
20 oktober 2026
Avslutad studie (Beräknad)
31 oktober 2026
Studieregistreringsdatum
Först inskickad
12 september 2026
Först inskickad som uppfyllde QC-kriterierna
12 september 2026
Första postat (Faktisk)
16 september 2026
Uppdateringar av studier
Senaste uppdatering publicerad (Faktisk)
16 september 2026
Senaste inskickade uppdateringen som uppfyllde QC-kriterierna
12 september 2026
Senast verifierad
1 september 2026
Mer information
Termer relaterade till denna studie
Nyckelord
Andra studie-ID-nummer
- 2026-P2-308-01
Plan för individuella deltagardata (IPD)
Planerar du att dela individuella deltagardata (IPD)?
NEJ
Läkemedels- och apparatinformation, studiedokument
Studerar en amerikansk FDA-reglerad läkemedelsprodukt
Nej
Studerar en amerikansk FDA-reglerad produktprodukt
Nej
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