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Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study.

Marek Biroš1, Daniel Kvak1,2, Jakub Dandár1

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Summary
This summary is machine-generated.

A new deep learning algorithm for mammographic breast density assessment shows accuracy comparable to radiologists. This automated tool can improve consistency and accuracy in breast cancer risk evaluation.

Keywords:
BI-RADSbreast densitycomputer-aided diagnosisdeep learningfull-field digital mammographymedical image processing

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Oncology and Cancer Research

Background:

  • Mammographic breast density is a key breast cancer risk factor.
  • Current visual assessment by radiologists has significant interobserver variability.
  • Inconsistent density assessment impacts breast cancer risk stratification.

Purpose of the Study:

  • To develop and evaluate a deep learning-based automatic detection algorithm (DLAD) for automated breast density assessment.
  • To compare the performance of DLAD against experienced radiologists.
  • To improve the accuracy and consistency of breast density evaluation.

Main Methods:

  • A multicentric dataset of 122 full-field digital mammography studies (488 images) was used.
  • Ground truth was established by two experienced radiologists for 72 studies.
  • DLAD performance was compared to five independent radiologists using accuracy, F1 score, precision, recall, and Cohen's Kappa.

Main Results:

  • DLAD achieved an accuracy of 0.819 and a Cohen's Kappa of 0.708.
  • The algorithm's performance matched or exceeded that of individual radiologists in several metrics.
  • Statistical analysis showed no significant difference in accuracy between DLAD and radiologists.

Conclusions:

  • The DLAD demonstrates robust and competitive performance in breast density assessment.
  • Automated evaluation using DLAD can enhance accuracy and consistency compared to manual methods.
  • This algorithm offers a reliable tool to improve breast cancer screening outcomes.