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PACS-integrated machine learning breast density classifier: clinical validation.

John Lewin¹, Sven Schoenherr², Martin Seebass²

¹Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States of America.

Clinical Imaging

|July 8, 2023

Summary

A new artificial intelligence tool accurately assesses breast density, showing high agreement with radiologists. This machine learning model predicts Breast Imaging Reporting and Data System (BI-RADS) density categories, aiding in mammographic analysis.

Keywords:

Artificial intelligence Breast density Mammography

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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Healthcare
Machine Learning Applications

Background:

Accurate breast density assessment is crucial for mammography interpretation and breast cancer risk stratification.
Current methods rely on subjective radiologist interpretation, leading to inter-observer variability.
Novel AI tools offer potential for objective and consistent breast density classification.

Purpose of the Study:

To evaluate the performance of a novel machine learning-based tool for predicting Breast Imaging Reporting and Data System (BI-RADS) breast density.
To assess the accuracy of the AI tool against radiologist consensus in diverse clinical settings.

Main Methods:

A convolutional neural network was trained on 33,000 mammographic examinations from one academic medical center (Site A).
The AI tool's performance was validated on separate datasets of 500 studies from Site A and 700 studies from a second academic medical center (Site B).
Radiologist consensus served as the ground truth for performance evaluation at both sites.

Main Results:

The AI classifier achieved an accuracy of 84.6% (Site A) and 89.7% (Site B) for four-category BI-RADS density classification.
For binary classification (dense vs. non-dense), accuracies were 94.4% (Site A) and 97.4% (Site B).
The AI classifier never disagreed with the consensus reading by more than one density category.

Conclusions:

The automated breast density tool demonstrates high agreement with expert radiologists' assessments.
This AI-powered tool shows significant potential for improving the consistency and accuracy of breast density evaluations in mammography.