An exploratory deep learning approach to investigate tuberculosis pathogenesis in nonhuman primate model: Combining automated radiological analysis with clinical and biomarkers data.

Journal: Journal of medical primatology
Published Date: Aug 1, 2024

Abstract

BACKGROUND: Tuberculosis (TB) kills approximately 1.6 million people yearly despite the fact anti-TB drugs are generally curative. Therefore, TB-case detection and monitoring of therapy, need a comprehensive approach. Automated radiological analysis, combined with clinical, microbiological, and immunological data, by machine learning (ML), can help achieve it.

Authors

  • Faisal Yaseen
    Department of Biomedical and Health Informatics, University of Washington, Seattle, Washington, USA.
  • Murtaza Taj
    Department of Computer Science, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences (LUMS), Lahore, Pakistan.
  • Resmi Ravindran
    Department of Pathology and Laboratory Medicine, University of California Davis, 4400 V Street, Sacramento, CA, 95817, USA.
  • Fareed Zaffar
    Department of Computer Science, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences (LUMS), Lahore, Pakistan.
  • Paul A Luciw
    Department of Pathology and Laboratory Medicine, University of California, Sacramento, California, USA.
  • Aamer Ikram
    National Institutes of Health, Islamabad, Pakistan.
  • Saerah Iffat Zafar
    Armed Forces Institute of Radiology and Imaging (AFIRI), Rawalpindi, Pakistan.
  • Tariq Gill
    Albany Medical Center, Albany, New York, USA.
  • Michael Hogarth
    Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, United States.
  • Imran H Khan
    Department of Pathology and Laboratory Medicine, University of California Davis, 4400 V Street, Sacramento, CA, 95817, USA. ihkhan@ucdavis.edu.

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