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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Artificial intelligence in digital breast pathology: Techniques and applications.

Asmaa Ibrahim1, Paul Gamble2, Ronnachai Jaroensri2

  • 1Department of Histopathology, Division of Cancer and Stem Cells, School of Medicine, The University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, NG5 1PB, UK.

Breast (Edinburgh, Scotland)
|January 15, 2020
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Summary

Artificial intelligence (AI) in digital pathology offers improved accuracy for breast cancer detection, classification, and prognosis. This approach enhances diagnostic precision, addressing the need for more tailored breast tumor treatments.

Keywords:
(Artificial intelligence)(Deep learning)(Machine learning)(Whole slide image)AIApplicationsBreast cancerBreast pathologyDLDigitalMLPathologyWSI

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Area of Science:

  • Oncology
  • Pathology
  • Artificial Intelligence

Background:

  • Breast cancer is a leading cause of cancer death globally, with histopathology as the primary diagnostic method.
  • Current diagnostic methods have limitations in precision due to the complex nature of cancer and tailored therapy needs.
  • Digital pathology and AI offer a promising avenue for enhancing breast cancer diagnosis and prognosis.

Purpose of the Study:

  • To review the current and prospective applications of artificial intelligence (AI) in digital pathology for breast cancer.
  • To explain the fundamental principles of digital pathology and AI in this context.
  • To identify and discuss the challenges that remain in the field of AI-driven breast cancer diagnostics.

Main Methods:

  • Review of current literature on artificial intelligence applications in digital breast cancer pathology.
  • Explanation of core concepts in digital pathology, including slide digitization and image analysis.
  • Discussion of AI algorithms and their role in tumor detection, classification, and behavior prediction.

Main Results:

  • AI in digital pathology shows potential for more accurate breast cancer detection and classification.
  • AI can aid in predicting tumor behavior, guiding personalized treatment strategies.
  • The integration of AI promises to improve diagnostic precision beyond traditional histopathology.

Conclusions:

  • AI holds significant promise for advancing breast cancer diagnostics through digital pathology.
  • Further research and development are needed to overcome current challenges and fully realize AI's potential.
  • AI-powered digital pathology is poised to transform breast cancer management and patient outcomes.