Artificial intelligence-based digital pathology for the detection and quantification of soil-transmitted helminths eggs.

Journal: PLoS neglected tropical diseases
PMID: 39348405

Abstract

BACKGROUND: Conventional microscopy of Kato-Katz (KK1.0) thick smears, the primary method for diagnosing soil-transmitted helminth (STH) infections, has limited sensitivity and is error-prone. Artificial intelligence-based digital pathology (AI-DP) may overcome the constraints of traditional microscopy-based diagnostics. This study in Ucayali, a remote Amazonian region of Peru, compares the performance of AI-DP-based Kato-Katz (KK2.0) method to KK1.0 at diagnosing STH infections in school-aged children (SAC).

Authors

  • Nancy Cure-Bolt
    Janssen Research & Development, LLC, Titusville, New Jersey, United States of America.
  • Fernando Pérez
    Genética, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, Cartagena 30202, Spain. fernando.perez8@um.es.
  • Lindsay A Broadfield
    Enaiblers AB, Uppsala, Sweden.
  • Bruno Levecke
    Department of Translational Physiology, Infectiology and Public Health, Ghent University, Merelbeke, Belgium.
  • Peter Hu
    Janssen Research & Development, LLC, Raritan, New Jersey, United States of America.
  • John Oleynick
    Janssen Research & Development, LLC, Springhouse, Pennsylvania, United States of America.
  • María Beltrán
    Biologist, Independent Researcher and Consultant, Lima, Perú.
  • Peter Ward
    Enaiblers, Uppsala, Sweden.
  • Lieven Stuyver
    Scientific Advisor, Zottegem, Belgium.

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