Automated Reports Generation of Cardiovascular Magnetic Resonance Imaging

NCT07340762 | Active Not Recruiting

• 1 other identifier: CMR_AutoReport
• Study Type: observational
• Target: 20,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

HCM - Hypertrophic Cardiomyopathy

Outcome Measures

Primary Outcomes (1)

• Diagnostic Accuracy of AI-Generated Cardiac MRI Reports

  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

Interventions

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.

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Chinese Academy of Medical Sciences, Fuwai Hospital: lead

Study Sites (1)

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

Beijing, Beijing Municipality, 100037, China

Location

MeSH Terms

Conditions

Cardiomyopathy, Hypertrophic

Condition Hierarchy (Ancestors)

Cardiomyopathies; Heart Diseases; Cardiovascular Diseases; Aortic Stenosis, Subvalvular; Aortic Valve Stenosis; Aortic Valve Disease; Heart Valve Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief Doctor

Study Record Dates

• First Submitted: January 5, 2026
• First Posted: January 14, 2026
• Study Start: October 1, 2025
• Primary Completion (Estimated): October 1, 2027
• Study Completion (Estimated): January 1, 2028
• Last Updated: January 21, 2026

Record last verified: 2026-01

Locations

CN (1)