Condition: Breast Neoplasms · Breast Diseases · Ultrasonography · Sponsor: Peking Union Medical College Hospital
This single-center, retrospective, observational study aims to construct a standardized benchmark evaluation system for intelligent breast ultrasound image interpretation and to systematically assess the diagnostic performance of current mainstream multimodal artificial intelligence (AI) models. De-identified B-mode breast ultrasound images with confirmed pathological diagnoses will be retrospectively collected from the institutional archive (2018-2025) and supplemented with images from published open-access datasets. Expert radiologists with varying experience levels will independently annotate all images according to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) v2025 criteria, including glandular tissue composition, lesion characterization (mass vs. non-mass lesion), morphological descriptors, and final BI-RADS classification. Baseline deep learning models (CNN-based ResNet-50 and Transformer-based USFM) will be trained to establish performance baselines and to stratify cases by diagnostic difficulty through cross-architecture consensus. Multiple multimodal large language models (MLLMs), including both general-purpose and medical-domain models, will then be evaluated via standardized API calls using BI-RADS-guided chain-of-thought prompts at temperature 0 for reproducibility. Primary endpoints include BI-RADS classification accuracy and diagnostic AUC for benign-malignant differentiation. Model robustness and safety will be a…
This description comes directly from the study's public registry record.
Qingli Zhu, MD · +86 13621376699 · zqlpumch@126.com
Yinglan Wu, MD · +86 15626121076 · wuylan7@gmail.com
Always discuss trial participation with your own doctor first.
| Peking Union Medical College Hospital | Beijing, China | Recruiting |
Get one email when the public record changes — results posted, or the study's status changes. Nothing else, ever.
We email about this public record only. Unsubscribe anytime with one click. Never medical advice.
This page is independently generated by Eichor from the public ClinicalTrials.gov record and re-synced daily. It is not the sponsor's official website unless claimed. Nothing here is medical advice; eligibility is always determined by the study team — talk to your own doctor first.
Source record: clinicaltrials.gov/study/NCT07500428