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Construction of a Benchmark for Breast Ultrasound AI Interpretation and Performance Evaluation of Multimodal AI Models

Recruitingn/aAges 18 Years - 75 YearsFemalePeking Union Medical College Hospital
NCT07500428 on ClinicalTrials.gov ↗
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In plain words

What the trial is, and whether it could suit you

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 de

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📋 Eligibility rules (as published)
Inclusion Criteria: * B-mode breast ultrasound grayscale images from the institutional PACS database or from published open-access breast ultrasound datasets with documented original institutional ethics approval * Image quality adequate for clinical diagnosis with clear visualization of the region of interest * Pathological diagnosis confirmed (for benign and malignant lesion groups), or normal breast status confirmed by a senior radiologist with \>15 years of breast ultrasound experience (for the normal group) * Complete de-identification with removal of all personally identifiable information Exclusion Criteria: * Severely degraded image quality precluding meaningful BI-RADS assessment * Duplicate images from the same patient (only the most representative image retained per lesion) * Images with residual personally identifiable information after de-identification processing * Cases with ambiguous, disputed, or unavailable pathological results * Non-B-mode ultrasound images, including elastography, contrast-enhanced ultrasound, and Doppler imaging
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What it tests

  • Multimodal AI Model Diagnostic Evaluation
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Where

1 site
Peking Union Medical College Hospital
Beijing, China
🤝 Taking part is your choice, and you can leave at any time. Only the trial team can confirm you are eligible — always decide together with your own doctor.
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