Related Experiment Video
Updated: Jun 13, 2026
Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform
Published on: November 28, 2018
A comprehensive evaluation of an artificial intelligence based digital pathology to monitor large-scale deworming
Peter K Ward1,2,3, Sara Roose1, Mio Ayana4
1Department of Translational Physiology, Infectiology and Public Health, Ghent University, Merelbeke, Belgium.
Background:
Manual 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.
Methods:
Here, 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.
Discussion:
This 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 Registration:
The trial was registered on September 29, 2023 Clinicaltrials.gov (ID: NCT06055530).
More Related Videos
Related Concept Videos
01:27Microorganisms in Agriculture and Food industry
01:18Production of Biopesticides