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Artificial intelligence and deep learning to map immune cell types in inflamed human tissue.

Kayla Van Buren1, Yi Li2, Fanghao Zhong2

  • 1Colton Center for Autoimmunity, NYU Grossman School of Medicine, New York, NY, United States of America.

Journal of Immunological Methods
|February 8, 2022
PubMed
Summary

A new deep learning algorithm accurately identifies immune cells, including rare T follicular helper (Tfh) cells, in inflammatory disease biopsies. This method quantifies cellular data for improved analysis of immune responses in conditions like dermatomyositis.

Keywords:
B cellsDeep learningDermatomyositisHistologyMachine learningT cells

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Area of Science:

  • Immunology
  • Computational Pathology
  • Dermatology

Background:

  • Inflammatory tissue biopsies contain complex immune cell networks crucial for immune and autoimmune responses.
  • Standard histological examination has limitations in quantitating and categorizing the vast data within biopsy slides.
  • Unbiased and comprehensive analysis of cellular interactions in inflammatory lesions is needed.

Purpose of the Study:

  • To develop a deep learning algorithm for unbiased identification and classification of immune cells in inflammatory tissue biopsies.
  • To specifically detect and classify T follicular helper (Tfh) cell subsets and B cells in dermatomyositis biopsy images.
  • To enable quantitative analysis of cellular composition and spatial relationships in diseased tissues.

Main Methods:

  • Development of a deep learning algorithm for image analysis of biopsy tissues.
  • Application of the algorithm to identify and classify immune cell populations, including Tfh and B cells.
  • Focus on images from dermatomyositis patient biopsies.

Main Results:

  • The deep learning algorithm demonstrated strong performance in detecting and classifying immune cells.
  • The algorithm successfully identified rare Tfh cell subsets within the tissue microenvironment.
  • High accuracy was achieved in distinguishing between different immune cell types.

Conclusions:

  • Deep learning offers a powerful tool for comprehensive and unbiased analysis of immune cells in inflammatory biopsies.
  • The developed algorithm can quantify cellular data, providing insights beyond standard histological examination.
  • This approach has potential applications in various disease states, including autoimmune and inflammatory conditions, and facilitates spatial mapping of cell types.