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.