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A Deep Learning Model to Triage Screening Mammograms: A Simulation Study.

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  • 1From the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Mass (A.Y., T.S., R.B.); and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, WAC 240, Boston, Mass 02114-2698 (R.M., C.L.).

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A new deep learning (DL) model can identify cancer-free mammograms, potentially reducing radiologist workload. This AI tool improves specificity without compromising cancer detection sensitivity.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Deep learning (DL) shows promise in enhancing mammogram sensitivity but hasn't addressed radiologist specificity or efficiency.
  • Current screening mammography interpretation faces challenges in optimizing both accuracy and workflow speed.

Purpose of the Study:

  • To develop and evaluate a deep learning (DL) model for triaging mammograms as cancer-free.
  • To assess the impact of a DL-triage workflow on radiologist performance and efficiency.

Main Methods:

  • Retrospective analysis of 223,109 screening mammograms from 66,661 women (2009-2016).
  • Development and validation of a DL model to identify cancer-free mammograms.
  • Simulation of a DL-triage workflow where radiologists skipped DL-triaged negative cases.

Main Results:

  • The DL-simulated workflow allowed radiologists to read 80.7% of mammograms.
  • Specificity improved from 93.5% to 94.2% (P = .002) in the DL workflow.
  • Sensitivity remained non-inferior (90.1% vs 90.6%) within a 5% margin (P < .001).

Conclusions:

  • The developed deep learning model can effectively triage mammograms, identifying cancer-free cases.
  • This AI-driven approach has the potential to significantly reduce radiologist workload.
  • The DL model improves specificity while maintaining high sensitivity, offering a promising tool for breast cancer screening.