Automated cooling tower detection through deep learning for Legionnaires' disease outbreak investigations: a model development and validation study.

Journal: The Lancet. Digital health
PMID: 38906615

Abstract

BACKGROUND: Cooling towers containing Legionella spp are a high-risk source of Legionnaires' disease outbreaks. Manually locating cooling towers from aerial imagery during outbreak investigations requires expertise, is labour intensive, and can be prone to errors. We aimed to train a deep learning computer vision model to automatically detect cooling towers that are aerially visible.

Authors

  • Karen K Wong
    Epic Systems, Verona, USA.
  • Thaddeus Segura
    University of California, Berkeley, CA, USA.
  • Gunnar Mein
    University of California, Berkeley, CA, USA.
  • Jia Lu
    College of Veterinary Medicine, Gansu Agricultural University, Lanzhou, China.
  • Elizabeth J Hannapel
    Centers for Disease Control and Prevention, Atlanta, GA, USA.
  • Jasen M Kunz
    Centers for Disease Control and Prevention, Atlanta, GA, USA.
  • Troy Ritter
    Centers for Disease Control and Prevention, Atlanta, GA, USA.
  • Jessica C Smith
    Centers for Disease Control and Prevention, Atlanta, GA, USA.
  • Alberto Todeschini
    School of Information, University of California, Berkeley, Berkeley, CA 94720, USA.
  • Fred Nugen
    Department of Radiology, Mayo Clinic, Rochester, MN, USA.
  • Chris Edens
    Centers for Disease Control and Prevention, Atlanta, GA, USA.

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