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Reduction of False-Positive Markings on Mammograms: a Retrospective Comparison Study Using an Artificial

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Artificial intelligence-based computer-aided detection (AI-CAD) significantly reduces false positives on mammograms compared to conventional CAD. This AI advancement improves efficiency and offers economic benefits in breast cancer screening.

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Breast Cancer Screening

Background:

  • Conventional computer-aided detection (CAD) systems on mammograms can generate false positives, increasing radiologist workload and patient anxiety.
  • The need for more efficient and accurate breast cancer detection tools is critical for improving screening outcomes.

Purpose of the Study:

  • To evaluate if an artificial intelligence (AI)-based CAD software can reduce false positive marks per image (FPPI) on mammograms.
  • To compare the performance of AI-CAD against an FDA-approved conventional CAD in terms of sensitivity, specificity, and FPPI.

Main Methods:

  • A retrospective study analyzed 250 full-field digital mammograms acquired between January and March 2013.
  • The number of marked regions of interest, FPPI, and mark-free cases were compared between AI-CAD and conventional CAD systems.
  • Sensitivity and specificity in cancer detection were assessed for both systems.

Main Results:

  • AI-CAD demonstrated a statistically significant reduction in FPPI compared to conventional CAD (95% confidence interval), with no compromise in sensitivity.
  • An overall 69% reduction in FPPI was observed with AI-CAD, including an 83% reduction for calcifications and 56% for masses.
  • 48% of cases had no AI-CAD markings, versus only 17% with no conventional CAD marks, indicating higher specificity.

Conclusions:

  • AI-based CAD significantly reduces false positives on mammograms for both calcifications and masses across all tissue densities.
  • The substantial decrease in FPPI suggests potential for reduced radiologist reading time and significant social and economic benefits in screening mammography.
  • AI-CAD represents a promising advancement for improving the efficiency and accuracy of breast cancer screening.