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Artificial intelligence and machine learning in nephropathology.

Jan U Becker1, David Mayerich2, Meghana Padmanabhan2

  • 1Institute of Pathology, University Hospital of Cologne, Cologne, Germany.

Kidney International
|June 2, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances kidney biopsy analysis by improving diagnostic accuracy and reproducibility. This technology, particularly convolutional neural networks, shows promise in nephropathology, aiding specialists rather than replacing them.

Keywords:
artificial intelligencecomputerconvolutional neural networkimage recognitionnephropathology

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Area of Science:

  • Nephropathology
  • Digital Pathology
  • Medical Artificial Intelligence

Background:

  • Artificial intelligence (AI) offers novel tools for analyzing kidney biopsies, traditionally a manual process.
  • Advancements in digital slide scanners, data storage, and computing power facilitate AI integration in pathology.
  • AI applications in nephropathology are emerging, with a focus on whole slide image analysis.

Purpose of the Study:

  • To review the current state and future potential of AI in nephropathology.
  • To explore AI's role in enhancing diagnostic, prognostic, and therapeutic information extraction from kidney biopsies.
  • To discuss challenges and concerns regarding AI implementation in nephropathology.

Main Methods:

  • Focus on AI applications analyzing whole slide images of kidney biopsies.
  • Utilizing advanced machine learning architectures, specifically convolutional neural networks.
  • Reviewing existing AI applications, with an emphasis on oncopathology examples.

Main Results:

  • AI demonstrates potential to improve the reproducibility of nephropathology results.
  • Convolutional neural networks are the state-of-the-art for AI in this field.
  • AI applications are currently in early stages but show significant promise, particularly in oncopathology.

Conclusions:

  • AI is expected to gradually integrate into routine diagnostics and research in nephropathology.
  • AI tools will augment, not replace, the capabilities of nephropathologists.
  • Addressing technological, stakeholder, and regulatory concerns is crucial for AI adoption.