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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Mammographic breast density is a key factor in breast cancer risk assessment.
  • Accurate breast density assessment is crucial for mammography interpretation and patient management.

Purpose of the Study:

  • To develop and validate a deep learning (DL) algorithm for assessing mammographic breast density.
  • To compare the DL algorithm's performance against radiologist interpretations.

Main Methods:

  • A deep convolutional neural network was trained on 41,479 digital screening mammograms.
  • The algorithm's performance was evaluated on a test set and in reader studies with radiologists.
  • Agreement was assessed using linear-weighted kappa statistics.

Main Results:

  • The DL model demonstrated good agreement with radiologists (κ = 0.67 in test set, κ = 0.78 in reader study).
  • Very good agreement (κ = 0.85) was observed in clinical implementation, with 94% of DL assessments accepted by radiologists.
  • The algorithm achieved performance comparable to experienced mammographers.

Conclusions:

  • The developed deep learning algorithm is effective for assessing mammographic breast density.
  • This AI tool can assist radiologists in routine clinical practice.
  • The DL model shows potential to standardize breast density assessment.