Automated Reports Generation of Cardiovascular Magnetic Resonance Imaging

January 18, 2026 updated by: Minjie Lu, Chinese Academy of Medical Sciences, Fuwai Hospital

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

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.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

20000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Beijing Municipality
      • Beijing, Beijing Municipality, China, 100037
        • Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

The study population consists of patients who underwent routine, clinically indicated cardiac magnetic resonance (CMR) examinations at a medical center,represents a real-world clinical population undergoing cardiac MRI for diagnostic evaluation of various cardiovascular diseases.

All CMR studies included in this observational study are retrospectively collected, fully de-identified, and accompanied by corresponding radiologist-authored clinical reports. The study population represents a real-world clinical cohort with a range of cardiac conditions commonly evaluated by CMR.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
Baseline

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Chinese Academy of Medical Sciences, Fuwai Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 1, 2025

Primary Completion (Estimated)

October 1, 2027

Study Completion (Estimated)

January 1, 2028

Study Registration Dates

First Submitted

January 5, 2026

First Submitted That Met QC Criteria

January 5, 2026

First Posted (Actual)

January 14, 2026

Study Record Updates

Last Update Posted (Actual)

January 21, 2026

Last Update Submitted That Met QC Criteria

January 18, 2026

Last Verified

January 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • CMR_AutoReport

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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