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Influence of Mammography Acquisition Parameters on AI and Radiologist Interpretive Performance.

William Lotter1,2,3, Daniel S Hippe4, Thomas Oshiro5

  • 1Department of Data Science, Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA 02215.

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This summary is machine-generated.

Mammography acquisition parameters significantly affect how well artificial intelligence (AI) and radiologists interpret screening mammograms. While both AI and human performance are impacted, certain parameters influence them differently, highlighting the need for standardized protocols in AI robustness.

Keywords:
AI RobustnessMammographyMedical Physics

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Screening mammography is crucial for early breast cancer detection.
  • The performance of interpretive tools, including artificial intelligence (AI), can be influenced by image acquisition parameters.
  • Understanding these influences is vital for ensuring consistent and reliable diagnostic accuracy.

Purpose of the Study:

  • To investigate the effect of mammography acquisition parameters on the interpretive performance of AI models and radiologists.
  • To identify specific parameters that significantly impact sensitivity and specificity for both AI and human readers.

Main Methods:

  • Retrospective analysis of 28,278 2D screening mammograms from 22,626 women acquired between 2010 and 2019.
  • Evaluation of seven acquisition parameters: machine version, kilovoltage peak, x-ray exposure, relative x-ray exposure, paddle size, compression force, and breast thickness.
  • Statistical analysis using generalized estimating equations to assess associations between parameters and AI/radiologist performance (sensitivity, specificity).

Main Results:

  • Acquisition parameters significantly impacted both AI and radiologist performance, with effects up to 10% in sensitivity and 5% in specificity.
  • Increased x-ray exposure reduced specificity for ensemble AI but not radiologists.
  • Increased compression force reduced specificity for radiologists but not for AI.

Conclusions:

  • Mammography acquisition parameters demonstrably influence the interpretive performance of both AI systems and radiologists.
  • Differences in parameter impact between AI and radiologists underscore the need for careful consideration of acquisition variability.
  • Further research into AI robustness and standardized acquisition protocols is warranted for optimal screening mammography interpretation.