DeepMedFake-Assisted Detection of Synthetic and Discordant Medical Images
12 de setembro de 2026 atualizado por: 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.
Visão geral do estudo
Status
Ainda não está recrutando
Condições
Intervenção / Tratamento
Tipo de estudo
Intervencional
Inscrição (Estimado)
64
Estágio
- Não aplicável
Contactos e Locais
Esta seção fornece os detalhes de contato para aqueles que conduzem o estudo e informações sobre onde este estudo está sendo realizado.
Contato de estudo
- Nome: Han Lv
- Número de telefone: +8613901073227
- E-mail: chrislvhan@126.com
Locais de estudo
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Beijing Municipality
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Beijing, Beijing Municipality, China, 100050
- Beijing Friendship hospital, Capital Medical University
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Contato:
- Han Lv, Professor
- Número de telefone: +8613901073227
- E-mail: chrislvhan@126.com
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Critérios de participação
Os pesquisadores procuram pessoas que se encaixem em uma determinada descrição, chamada de critérios de elegibilidade. Alguns exemplos desses critérios são a condição geral de saúde de uma pessoa ou tratamentos anteriores.
Critérios de elegibilidade
Idades elegíveis para estudo
- Adulto
- Adulto mais velho
Aceita Voluntários Saudáveis
Sim
Descrição
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.
Plano de estudo
Esta seção fornece detalhes do plano de estudo, incluindo como o estudo é projetado e o que o estudo está medindo.
Como o estudo é projetado?
Detalhes do projeto
- Finalidade Principal: Outro
- Alocação: Randomizado
- Modelo Intervencional: Atribuição cruzada
- Mascaramento: Solteiro
Armas e Intervenções
Grupo de Participantes / Braço |
Intervenção / Tratamento |
|---|---|
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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.
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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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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.
|
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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.
|
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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.
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O que o estudo está medindo?
Medidas de resultados primários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
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Reader Performance With and Without DeepMedFake Assistance: Sensitivity of the Final Verification Decision in the Hospital Clinical Cohort
Prazo: 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
Prazo: 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 healthcare audit professionals 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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Medidas de resultados secundários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
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Reader Performance With and Without DeepMedFake Assistance: Specificity of the Final Verification Decision in the Hospital Clinical Cohort
Prazo: 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
Prazo: 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
Prazo: 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
Prazo: 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
Prazo: 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
Prazo: 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
Prazo: 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
Prazo: 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
Prazo: 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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Colaboradores e Investigadores
É aqui que você encontrará pessoas e organizações envolvidas com este estudo.
Patrocinador
Datas de registro do estudo
Essas datas acompanham o progresso do registro do estudo e os envios de resumo dos resultados para ClinicalTrials.gov. Os registros do estudo e os resultados relatados são revisados pela National Library of Medicine (NLM) para garantir que atendam aos padrões específicos de controle de qualidade antes de serem publicados no site público.
Datas Principais do Estudo
Início do estudo (Estimado)
20 de setembro de 2026
Conclusão Primária (Estimado)
20 de outubro de 2026
Conclusão do estudo (Estimado)
31 de outubro de 2026
Datas de inscrição no estudo
Enviado pela primeira vez
12 de setembro de 2026
Enviado pela primeira vez que atendeu aos critérios de CQ
12 de setembro de 2026
Primeira postagem (Real)
16 de setembro de 2026
Atualizações de registro de estudo
Última Atualização Postada (Real)
16 de setembro de 2026
Última atualização enviada que atendeu aos critérios de controle de qualidade
12 de setembro de 2026
Última verificação
1 de setembro de 2026
Mais Informações
Termos relacionados a este estudo
Palavras-chave
Outros números de identificação do estudo
- 2026-P2-308-01
Plano para dados de participantes individuais (IPD)
Planeja compartilhar dados de participantes individuais (IPD)?
NÃO
Informações sobre medicamentos e dispositivos, documentos de estudo
Estuda um medicamento regulamentado pela FDA dos EUA
Não
Estuda um produto de dispositivo regulamentado pela FDA dos EUA
Não
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