PLoS ONE (Jan 2024)

A comprehensive evaluation of an artificial intelligence based digital pathology to monitor large-scale deworming programs against soil-transmitted helminths: A study protocol.

  • Peter K Ward,
  • Sara Roose,
  • Mio Ayana,
  • Lindsay A Broadfield,
  • Peter Dahlberg,
  • Narcis Kabatereine,
  • Adama Kazienga,
  • Zeleke Mekonnen,
  • Betty Nabatte,
  • Lieven Stuyver,
  • Fiona Vande Velde,
  • Sofie Van Hoecke,
  • Bruno Levecke

DOI
https://doi.org/10.1371/journal.pone.0309816
Journal volume & issue
Vol. 19, no. 10
p. e0309816

Abstract

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BackgroundManual screening of a Kato-Katz (KK) thick stool smear remains the current standard to monitor the impact of large-scale deworming programs against soil-transmitted helminths (STHs). To improve this diagnostic standard, we recently designed an artificial intelligence based digital pathology system (AI-DP) for digital image capture and analysis of KK thick smears. Preliminary results of its diagnostic performance are encouraging, and a comprehensive evaluation of this technology as a cost-efficient end-to-end diagnostic to inform STH control programs against the target product profiles (TPP) of the World Health Organisation (WHO) is the next step for validation.MethodsHere, we describe the study protocol for a comprehensive evaluation of the AI-DP based on its (i) diagnostic performance, (ii) repeatability/reproducibility, (iii) time-to-result, (iv) cost-efficiency to inform large-scale deworming programs, and (v) usability in both laboratory and field settings. For each of these five attributes, we designed separate experiments with sufficient power to verify the non-inferiority of the AI-DP (KK2.0) over the manual screening of the KK stool thick smears (KK1.0). These experiments will be conducted in two STH endemic countries with national deworming programs (Ethiopia and Uganda), focussing on school-age children only.DiscussionThis comprehensive study will provide the necessary data to make an evidence-based decision on whether the technology is indeed performant and a cost-efficient end-to-end diagnostic to inform large-scale deworming programs against STHs. Following the protocolized collection of high-quality data we will seek approval by WHO. Through the dissemination of our methodology and statistics, we hope to support additional developments in AI-DP technologies for other neglected tropical diseases in resource-limited settings.Trial registrationThe trial was registered on September 29, 2023 Clinicaltrials.gov (ID: NCT06055530).