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Artificial intelligence in breast imaging.

E P V Le1, Y Wang2, Y Huang3

  • 1University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK; EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging, University of Cambridge, Cambridge CB3 0WA, UK.

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Computer-aided detection (CAD) systems in breast imaging are evolving from traditional methods to deep learning (DL) approaches. Artificial intelligence (AI)-CAD tools show promise but require rigorous clinical validation and explainability for safe implementation.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional computer-aided detection (CAD) systems in mammography utilize rule-based approaches and classical machine learning.
  • These systems have demonstrated limitations, including adverse effects on radiologist performance and increased recall rates.
  • The Digital Mammography DREAM Challenge highlighted the potential of deep learning (DL) for improving breast cancer screening accuracy.

Purpose of the Study:

  • To review current limitations and future opportunities for computer-aided detection (CAD) systems and artificial intelligence (AI) in breast imaging.
  • To discuss the transition from traditional CAD to AI-driven solutions, particularly deep learning.
  • To identify gaps and requirements for the clinical implementation of AI-CAD tools.

Main Methods:

  • Review of existing literature on CAD and AI in breast imaging.
  • Analysis of outcomes from the Digital Mammography DREAM Challenge.
  • Discussion of emerging AI-CAD systems and their applications across various imaging modalities.

Main Results:

  • Deep learning, particularly convolutional neural networks, has shown superior performance in breast cancer screening compared to traditional CAD.
  • New AI-CAD systems are being developed for digital breast tomosynthesis, ultrasound, and MRI.
  • A market gap exists for AI-CAD tools in contrast-enhanced spectral mammography.

Conclusions:

  • AI-CAD systems offer significant opportunities to enhance breast imaging analysis.
  • Clinical implementation necessitates robust testing in real-world scenarios, large datasets, and cost-effectiveness assessments.
  • Future AI-CAD systems must incorporate explainable AI (XAI) to ensure transparency and compliance with regulations like GDPR.