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DeepMedFake-Assisted Detection of Synthetic and Discordant Medical Images

2026年9月12日 更新者：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.

研究概览

地位

尚未招聘

条件

干预/治疗

研究类型

介入性

注册 (估计的)

64

阶段

  • 不适用

联系人和位置

本节提供了进行研究的人员的详细联系信息，以及有关进行该研究的地点的信息。

学习联系方式

学习地点

  • 中国
    • Beijing Municipality
      • Beijing、Beijing Municipality、中国、100050
        • Beijing Friendship hospital, Capital Medical University
        • 接触：

参与标准

研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

  • 成人
  • 年长者

接受健康志愿者

是的

描述

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.

学习计划

本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。

研究是如何设计的？

设计细节

  • 主要用途：其他
  • 分配：随机化
  • 介入模型：交叉作业
  • 屏蔽：单身的

武器和干预

参与者组/臂
干预/治疗
实验性的：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.
设备：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.
实验性的：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.
设备：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.
实验性的：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.
设备：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.
实验性的：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.
设备：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.

研究衡量的是什么？

主要结果指标

结果测量
措施说明
大体时间
Reader Performance With and Without DeepMedFake Assistance: Sensitivity of the Final Verification Decision in the Hospital Clinical Cohort
大体时间：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
大体时间：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

次要结果测量

结果测量
措施说明
大体时间
Reader Performance With and Without DeepMedFake Assistance: Specificity of the Final Verification Decision in the Hospital Clinical Cohort
大体时间：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
大体时间：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
大体时间：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
大体时间：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
大体时间：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
大体时间：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
大体时间：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
大体时间：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
大体时间：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

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

赞助

Beijing Friendship Hospital

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (估计的)

2026年9月20日

初级完成 (估计的)

2026年10月20日

研究完成 (估计的)

2026年10月31日

研究注册日期

首次提交

2026年9月12日

首先提交符合 QC 标准的

2026年9月12日

首次发布 (实际的)

2026年9月16日

研究记录更新

最后更新发布 (实际的)

2026年9月16日

上次提交的符合 QC 标准的更新

2026年9月12日

最后验证

2026年9月1日

更多信息

与本研究相关的术语

关键字

其他研究编号

  • 2026-P2-308-01

计划个人参与者数据 (IPD)

计划共享个人参与者数据 (IPD)？

不

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

此信息直接从 clinicaltrials.gov 网站检索，没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求，请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改，我们的网站上也会自动更新.

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