Prediction of mammographic breast density based on clinical breast ultrasound images using deep learning: a retrospective analysis.

ABSTRACT:

Background

Breast density, as derived from mammographic images and defined by the Breast Imaging Reporting & Data System (BI-RADS), is one of the strongest risk factors for breast cancer. Breast ultrasound is an alternative breast cancer screening modality, particularly useful in low-resource, rural contexts. To date, breast ultrasound has not been used to inform risk models that need breast density. The purpose of this study is to explore the use of artificial intelligence (AI) to predict BI-RADS breast density category from clinical breast ultrasound imaging.

Methods

We compared deep learning methods for predicting breast density directly from breast ultrasound imaging, as well as machine learning models from breast ultrasound image gray-level histograms alone. The use of

SUBMITTER: Bunnell A 

PROVIDER: S-EPMC12032905 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature