Automated Reports Generation of Cardiovascular Magnetic Resonance Imaging

The goal of this observational study is to evaluate the accuracy, completeness, and clinical consistency of large language model-generated cardiac magnetic resonance (CMR) imaging reports compared with expert radiologist reports in patients undergoing routine clinical CMR examinations.

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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Key information

Conditions

HCM - Hypertrophic Cardiomyopathy Aortic Stenosis, Subvalvular Aortic Valve Disease Aortic Valve Stenosis Cardiomyopathies Cardiomyopathy, Hypertrophic Cardiovascular Diseases Heart Diseases Heart Valve Diseases

Sex eligibility

All sexes

Study type

Observational

Primary location

Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College

Beijing, Beijing Municipality, 100037, China

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Patients who underwent clinically indicated cardiac magnetic resonance (CMR) examinations.
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

Incomplete or corrupted CMR image data.
Absence of a reference radiologist report.
Poor image quality that precludes reliable clinical interpretation.
CMR studies with severe imaging artifacts affecting diagnostic evaluation.

Treatment and study plan

large lanuage model

Other

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.

Primary outcomes

Diagnostic Accuracy of AI-Generated Cardiac MRI Reports

Time frame: Baseline

The primary outcome is the diagnostic accuracy of automatically generated cardiac magnetic resonance (CMR) reports produced by a large multimodal model.

Diagnostic accuracy is assessed by comparing AI-generated reports with reference reports written by board-certified cardiovascular radiologists. Agreement is evaluated at the level of key clinical findings and final imaging impressions, using predefined criteria.

Accuracy metrics include correctness of major diagnoses and presence or absence of clinically relevant imaging findings.

Sponsors and collaborators

Lead sponsor

Chinese Academy of Medical Sciences, Fuwai Hospital

Other

Registry information

Official study title

Multi-step Automated Report Generation of Cardiovascular Magnetic Resonance Imaging Based on Visual Large Language Model

Important dates

Study start: 2025
Primary completion: 2027
Study completion: 2028
First posted: Jan 14, 2026
Registry last updated: Jan 21, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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