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Improving the Performance of Radiologists Using Artificial Intelligence-Based Detection Support Software for

Jeong Hoon Lee1, Ki Hwan Kim1, Eun Hye Lee2

  • 1Lunit Inc., Seoul, Korea.

Korean Journal of Radiology
|April 18, 2022
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) significantly improved breast cancer detection performance for radiologists, enhancing sensitivity and accuracy. While AI reduced reading time for specialists, it increased it for general radiologists, indicating varied impacts.

Keywords:
Artificial intelligenceBreast cancerDeep-learningMammographyMulti-reader studyReading timeScreening

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Mammography is a key tool for breast cancer screening.
  • Radiologist performance in mammography interpretation can vary.
  • AI tools are being developed to assist in medical image analysis.

Purpose of the Study:

  • To evaluate the impact of AI software on radiologist performance in breast cancer detection.
  • To assess the effect of AI on the time efficiency of mammogram readings.
  • To compare AI's effectiveness with different levels of radiologist experience.

Main Methods:

  • A deep learning AI software was validated on mammography data from 200 patients.
  • Ten radiologists (5 breast specialists, 5 general) read mammograms with and without AI assistance.
  • Performance metrics (AUROC, sensitivity, specificity) and reading times were recorded and analyzed.

Main Results:

  • AI assistance significantly improved the area under the receiver operating characteristic curve (AUROC) for both breast specialist radiologists (BSRs) and general radiologists (GRs).
  • Sensitivity was notably enhanced by AI in both groups, while specificity remained largely unchanged.
  • AI reduced reading time for BSRs but increased it for GRs.

Conclusions:

  • AI-based software enhances the diagnostic performance of radiologists in breast cancer detection.
  • The impact of AI on reading time varies based on radiologist experience.
  • AI tools show promise in improving mammography interpretation accuracy.