An artificial intelligence-powered digital pathology platform to support large-scale deworming programs against soil-transmitted helminthiasis and intestinal schistosomiasis in resource-limited settings

Abstract

Background The World Health Organization (WHO) has emphasised the need for innovative diagnostic tools to support the control and elimination of neglected tropical diseases (NTDs). Microscopy-based diagnostics, the current standard, rely on trained technicians for labour-intensive processes, posing logistical challenges in the low-resource settings where NTDs are most prevalent. This study describes the technical details of an artificial intelligence-powered digital pathology (AI-DP) platform designed to support large-scale deworming programs for two NTDs, alongside its analytical performance and user experience in laboratory and field settings.

Methodology/principal findings The AI-DP platform integrates electronic data capture tools, whole-slide imaging scanners, onboard AI analysis, and result verification software to automate microscopy-based screening. Targeting soil-transmitted helminthiasis (STH) and intestinal schistosomiasis (SCH) as initial use cases, the system was deployed in Ethiopia and Uganda, scanning 951 Kato-Katz (KK) thick smears containing 43,919 verified helminth eggs. Using 5-fold cross-validation, precision/recall/average precision were 95.4%/91.7%/97.1% for Ascaris lumbricoides, 95.9%/86.7%/94.8% for Trichuris trichiura, 84.6%/86.6%/91.4% for hookworm, and 89.1%/79.1%/89.2% for Schistosoma mansoni. Feedback from 14 field users across 30 real-world scenarios indicated the AI-DP platform’s improved usability, particularly in hardware portability and software interfaces, though the average scan time of 12.5 minutes per smear was identified as a limitation.

Conclusions/significance The AI-DP platform demonstrates potential as a tool for efficient monitoring and evaluation of STH and SCH control programs by providing near-real-time data with quality controls. However, further validation studies are needed to assess its clinical diagnostic performance, field usability, and cost-effectiveness in large-scale STH and SCH deworming programs. Given that the platform also provides a pipeline for any microscopy-based diagnosis, its potential for other NTDs also needs further attention.

Author summary Intestine- and blood-dwelling parasitic afflict over a billion people in low-resource countries, yet diagnosis often depends on labour-intensive manual microscopy performed by trained technicians. We developed a portable, artificial intelligence–powered digital pathology (AI-DP) platform to automate this process, incorporating a field-deployable slide scanner, onboard AI for worm egg detection and identification in stool smears, and a user-friendly verification interface for technicians to review the results of AI. Designed with consideration of World Health Organization diagnostic needs for these diseases, the platform was tested in laboratory and zero-infrastructure field trials in Ethiopia and Uganda, and processed nearly 1,000 stool smears, detecting eggs of both intestinal-helminths and Schistosoma mansoni with over 90% precision and recall. Field users noted improvements in portability and ease of use, though scan times remain slower than manual microscopy. While the AI-DP platform shows potential as a tool for efficient monitoring and evaluation of worm control programs, with possible extension to other diseases, further validation studies are essential to evaluate its clinical diagnostic performance and cost-effectiveness in large-scale deworming initiatives.