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Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology.

Aleksandra Zuraw1, Michael Staup2, Robert Klopfleisch3

  • 1Pathology Department, 25913Charles River Laboratories, Frederick, MD, USA.

Toxicologic Pathology
|December 29, 2020
PubMed
Summary

Implementing a robust quality control system is crucial for accurate digital tissue image analysis in toxicology. This ensures reliable data extraction from whole-slide images for toxicopathologic studies.

Keywords:
artificial intelligencedigital pathologyhistopathologyimage analysisquality controlwhole slide images

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Area of Science:

  • Digital pathology
  • Computational pathology
  • Toxicologic pathology

Background:

  • Digital tissue image analysis extracts quantitative data from whole-slide images.
  • Quality control is essential for reliable results in digital pathology workflows.
  • Toxicologic pathologists must ensure the accuracy of digital image analysis data.

Purpose of the Study:

  • To describe common digital tissue image analysis endpoints and error sources.
  • To outline quality assurance approaches for digital image analysis in toxicology.
  • To adapt FDA regulatory frameworks for AI/ML software to toxicologic studies.

Main Methods:

  • Review of common digital tissue image analysis endpoints.
  • Identification of potential sources of analysis errors.
  • Adaptation of FDA regulatory framework for AI/ML software modifications.

Main Results:

  • Digital image analysis requires stringent quality control for accurate toxicologic data.
  • Common endpoints and error sources in digital pathology are identified.
  • Recommended quality assurance approaches are provided for classical and ML-based analysis.

Conclusions:

  • A well-designed quality control system is vital for digital pathology.
  • Toxicologic pathologists play a key role in ensuring the quality of image analysis.
  • Proposed approaches enhance the reliability of digital image analysis in toxicopathologic studies.