NCT07340762

Automated Reports Generation of Cardiovascular Magnetic Resonance Imaging

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

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 20,000 (actual)
• Sponsor: Chinese Academy of Medical Sciences, Fuwai Hospital · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Accepted

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

Interventions

Type	Name	Description
OTHER	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.

Timeline

• Start date: 2025-10-01
• Primary completion: 2027-10-01
• Completion: 2028-01-01
• First posted: 2026-01-14
• Last updated: 2026-01-21

Locations

1 site across 1 country: China