Automated Reports Generation of Cardiovascular Magnetic Resonance Imaging
NCT ID: NCT07340762
Last Updated: 2026-01-21
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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ACTIVE_NOT_RECRUITING
20000 participants
OBSERVATIONAL
2025-10-01
2028-01-01
Brief Summary
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The main question(s) it aims to answer are:
Can automatically generated CMR reports produced by a large multimodal model accurately reflect key imaging findings and diagnoses when compared with reports written by experienced cardiovascular radiologists?
How does the quality of generated reports perform in terms of clinical correctness, completeness, and linguistic clarity, as assessed by quantitative metrics and expert review?
If there is a comparison group:
Researchers will compare AI-generated CMR reports with ground-truth reports authored by board-certified cardiovascular radiologists to see if the automated system achieves comparable diagnostic accuracy and report quality across different cardiac pathologies.
Participants will:
Undergo standard-of-care cardiac MRI examinations as part of routine clinical practice.
Have their anonymized CMR image data and corresponding radiologist reports retrospectively collected.
Contribute data that will be used to generate automated CMR reports, which will then be evaluated against expert reports using objective metrics (e.g., diagnostic agreement, entity-level accuracy) and subjective clinical scoring by radiologists.
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Interventions
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large lanuage model
The intervention consists of an automated CMR report generation system based on a large multimodal deep learning model.
The model takes de-identified CMR image data as input, including standard clinical sequences (e.g., cine LGE), and automatically generates a free-text radiology report describing cardiac structure, function, and imaging findings.
The generated reports are produced offline and retrospectively, and are not used for clinical decision-making or patient management. No changes are made to the imaging acquisition protocol or standard clinical workflow.
For evaluation purposes, the AI-generated reports are compared with reference reports authored by experienced cardiovascular radiologists, using predefined quantitative accuracy metrics and expert qualitative assessment of clinical correctness, completeness, and readability.
This intervention is intended solely for research and performance evaluation of automated report generation and does not influence patient care.
Eligibility Criteria
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Inclusion Criteria
* Availability of complete and de-identified CMR image data.
* Availability of corresponding clinical CMR reports authored by experienced cardiovascular radiologists.
* CMR studies acquired using standard clinical imaging protocols.
Exclusion Criteria
* Absence of a reference radiologist report.
* Poor image quality that precludes reliable clinical interpretation.
* CMR studies with severe imaging artifacts affecting diagnostic evaluation.
ALL
Yes
Sponsors
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Chinese Academy of Medical Sciences, Fuwai Hospital
OTHER
Responsible Party
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Minjie Lu
Chief Doctor
Locations
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Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College
Beijing, Beijing Municipality, China
Countries
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Other Identifiers
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CMR_AutoReport
Identifier Type: -
Identifier Source: org_study_id
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