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Related Experiment Video

Updated: May 20, 2026

Detection of Abnormal Prion Protein by Immunohistochemistry
06:38

Detection of Abnormal Prion Protein by Immunohistochemistry

Published on: May 5, 2023

Augmenting prion surveillance by immunohistochemistry using artificial intelligence-based image analysis.

Liam E Broughton-Neiswanger1, David A Schneider1,2, Jodi D Smith3

  • 1Washington State University, Pullman, WA.

Veterinary Pathology
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

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A new deep learning model automates prion immunohistochemistry slide review, achieving 100% concordance for diagnosing transmissible spongiform encephalopathies like chronic wasting disease (CWD). This AI enhances diagnostic consistency and efficiency for veterinary laboratories.

Area of Science:

  • Veterinary Pathology
  • Prion Disease Research
  • Artificial Intelligence in Diagnostics

Background:

  • Transmissible spongiform encephalopathies, including chronic wasting disease (CWD) and scrapie, are caused by prions.
  • Immunohistochemistry (IHC) is the current gold standard for diagnosing prion diseases in formalin-fixed tissues.
  • Manual IHC slide review by veterinary pathologists is time-consuming and creates a diagnostic bottleneck.

Purpose of the Study:

  • To develop and validate a deep learning-based image analysis tool for automating prion IHC slide review.
  • To improve the efficiency and consistency of prion disease diagnostics in veterinary surveillance.
  • To address the need for scalable diagnostic capabilities in prion disease monitoring.

Main Methods:

  • A deep learning model was trained on 143 prion IHC whole-slide images with 3296 annotations.
Keywords:
chronic wasting diseaseconvolutional neural networkdeep learningdigital pathologyimmunohistochemistryprionscrapieveterinary diagnostics

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Last Updated: May 20, 2026

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  • Images were tiled and used to fine-tune a convolutional neural network for recognizing prion-specific features.
  • The model was tested on a blinded dataset of 50 CWD IHC slides.

    • Main Results:

    • The deep learning model achieved 100% concordance with veterinary pathologist evaluations for chromogenic labeling on CWD IHC slides.
    • The AI demonstrated the ability to recognize prion-specific quality control parameters and labeling patterns.
    • The automated approach significantly reduces the time required for initial slide review.

    Conclusions:

    • Deep learning-based image analysis offers a viable solution for automating prion IHC slide review.
    • This technology can enhance diagnostic consistency, improve laboratory efficiency, and support large-scale prion surveillance.
    • Automating this diagnostic step is crucial for timely detection and management of prion diseases in animal populations.