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Related Experiment Video

Updated: Jul 14, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Measuring Breast Density at Scale With AI: Insights From BreastScreen NSW.

Richard Walton1, Douglas Dunn2, Matthew Warner-Smith1,3

  • 1Cancer Institute NSW, St Leonards, New South Wales, Australia.

Journal of Medical Imaging and Radiation Oncology
|July 13, 2026
PubMed
Summary

Artificial intelligence (AI) shows reliable breast density estimation, comparable to radiologists. Breast density varies significantly by ancestry and age, with higher density noted in clients with implants.

Keywords:
AIancestrybreastdensitypopulation

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Breast density is a significant risk factor for breast cancer.
  • Dense breast tissue can obscure malignancies on mammograms.
  • Accurate breast density assessment is crucial for risk stratification and screening.

Purpose of the Study:

  • To evaluate the reliability of Lunit INSIGHT MMG for breast density estimation.
  • To assess breast density on a large population scale.
  • To compare breast density in clients with and without breast implants.

Main Methods:

  • Compared AI (Lunit INSIGHT MMG) and radiologist performance on 15,518 mammograms for intra- and inter-reader reliability.
  • Analyzed breast density variations in over 624,000 mammograms, stratified by birth country and age.
  • Assessed breast density in 4,047 clients with implants versus 396,301 clients without implants.

Main Results:

  • AI demonstrated good inter-reader reliability with radiologists (kappa=0.72).
  • Significant variations in breast density were observed across different ancestries and age groups.
  • Clients with breast implants exhibited higher breast density compared to age-matched controls.

Conclusions:

  • AI tools are adequate for reliable breast density assessment.
  • Age and ancestry are significant factors influencing breast density.
  • The study provides valuable insights from a large and diverse population dataset.