Artificial Intelligence-Based Screening for Mycobacteria in Whole-Slide Images of Tissue Samples.

Journal: American journal of clinical pathology
Published Date:

Abstract

OBJECTIVES: This study aimed to develop and validate a deep learning algorithm to screen digitized acid fast-stained (AFS) slides for mycobacteria within tissue sections.

Authors

  • Liron Pantanowitz
    Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
  • Uno Wu
    Department of Electrical Engineering, Molecular Biomedical Informatics Lab, National Cheng Kung University, Tainan City, Taiwan.
  • Lindsey Seigh
    Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
  • Edmund LoPresti
    Information Services Division, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
  • Fang-Cheng Yeh
    Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA. frank.yeh@pitt.edu.
  • Payal Salgia
    Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
  • Pamela Michelow
    Cytology Unit, Department of Anatomical Pathology, Faculty of Health Science, National Health Laboratory Service, University of the Witwatersrand, Johannesburg, South Africa.
  • Scott Hazelhurst
    School of Electrical & Information Engineering and Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa.
  • Wei-Yu Chen
    Department of Pathology, Wan Fang Hospital.
  • Douglas Hartman
    Department of Pathology, University of Pittsburgh Medical Center Cancer Pavilion, Suite 201, 5150 Centre Ave, Pittsburgh, PA, 15232, USA.
  • Chao-Yuan Yeh