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Performance of Two Deep Learning-based AI Models for Breast Cancer Detection and Localization on Screening Mammograms

Marit A Martiniussen1,2, Marthe Larsen3, Tone Hovda4

  • 1Department of Radiology, Østfold Hospital Trust, Kalnes, Norway.

Radiology. Artificial Intelligence
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This summary is machine-generated.

Two artificial intelligence (AI) models demonstrated strong performance in detecting breast cancer on screening mammograms, with accurate marking of cancer locations. These AI tools show promise for improving mammography interpretation and cancer diagnosis.

Keywords:
BreastComputed-aided DiagnosisMammographyScreening

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Screening mammography is crucial for early breast cancer detection.
  • Artificial intelligence (AI) offers potential to enhance mammogram interpretation accuracy.
  • Evaluating AI model performance in real-world screening settings is essential.

Purpose of the Study:

  • To assess the cancer detection and marker placement accuracy of two AI models for screening mammograms.
  • To compare the performance of a commercial AI model (Model A) and an in-house AI model (Model B).

Main Methods:

  • Retrospective analysis of 129,434 screening mammograms from BreastScreen Norway (2008-2018).
  • Calculation of area under the receiver operating characteristic curve (AUC) for both AI models.
  • Radiologic review to assess AI marking localization and classify interval cancers.

Main Results:

  • Both AI models achieved an AUC of 0.93, indicating high diagnostic performance.
  • Model A detected 82.5%-92.4% and Model B detected 81.8%-93.7% of screen-detected cancers at different thresholds.
  • AI markings were accurately localized for screen-detected cancers, and 79%-82% for interval cancers.

Conclusions:

  • Both evaluated AI models exhibit promising capabilities for cancer detection in screening mammography.
  • The AI-generated markings demonstrated good correspondence with actual cancer locations.
  • AI tools have the potential to aid radiologists in identifying subtle signs of malignancy on mammograms.