Automatic discovery of clinically interpretable imaging biomarkers for Mycobacterium tuberculosis supersusceptibility using deep learning.

Journal: EBioMedicine
Published Date: Dec 1, 2020

Abstract

BACKGROUND: Identifying which individuals will develop tuberculosis (TB) remains an unresolved problem due to few animal models and computational approaches that effectively address its heterogeneity. To meet these shortcomings, we show that Diversity Outbred (DO) mice reflect human-like genetic diversity and develop human-like lung granulomas when infected with Mycobacterium tuberculosis (M.tb) .

Authors

  • Thomas E Tavolara
    Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, USA.
  • M Khalid Khan Niazi
    Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, NC, United States of America.
  • Melanie Ginese
    Department of Infectious Disease and Global Health, Tufts University Cummings School of Veterinary Medicine, 200 Westboro Rd., North Grafton, MA 01536, United States.
  • Cesar Piedra-Mora
    Department of Biomedical Sciences, Tufts University Cummings School of Veterinary Medicine, 200 Westboro Rd., North Grafton, MA 01536, United States.
  • Daniel M Gatti
    The College of the Atlantic, 105 Eden Street, Bar Harbor, ME 04609, United States.
  • Gillian Beamer
    Department of Infectious Disease and Global Health, Tufts University Cummings School of Veterinary Medicine, 200 Westboro Rd., North Grafton, MA 01536, United States.
  • Metin N Gurcan
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA. Electronic address: metin.gurcan@osumc.edu.

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