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

Abstract 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 Clinicaltrials.gov (ID: NCT06055530 ). Author summary Millions of deworming tablets are annually administered to children to reduce the morbidity caused by intestinal worms. To monitor the progress of these large-scale deworming programs, periodic assessments are made regarding the occurrence and prevalence of intestinal worm infections. Manual examination of a stool smear through a compound microscope remains the current diagnostic standard. We recently developed a device that utilizes artificial intelligence (AI) to scan smears and recognize eggs of intestinal worms. Encouraging preliminary results of the diagnostic performance warrant additional and more research, essential for obtaining necessary approvals to support wide-scale adoption. Here, we describe the study protocols we will employ for a comprehensive evaluation of this AI-based device. The generated results will provide health decision-makers with evidence-based data to assess whether the tool can be recommended for informing large-scale deworming programs against intestinal worms. Additionally, we provide full access to our study documentation which may be relevant for evaluating other AI-based devices for intestinal worms.