An artificial intelligence-powered digital pathology platform to support large-scale deworming programs against soil-transmitted helminthiasis and intestinal schistosomiasis in resource-limited settings
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Background
The World Health Organization (WHO) has emphasised the need for innovative diagnostic tools to support the control and eliminate 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 whole slide imaging scanners, onboard AI analysis, electronic data capture tools, and result verification software to automate microscopy-based screening. Targeting soil-transmitted helminths (STH) and intestinal schistosomiasis (SCH) as initial use cases, the system was deployed in Ethiopia and Uganda, scanning 951 Kato-Katz (KK) slides 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 noted the AI-DP platform’s improved usability, particularly in hardware portability and software interfaces, though the average scan time of 12.5 minutes per slide was identified as a limitation compared to manual microscopy.
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 clinical diagnostic performance, field usability, and cost-effectiveness in large-scale STH and SCH deworming programs. A platform approach supports scalability to other microscopy-based diagnostics, aligning with global elimination goals for NTDs.
AUTHOR SUMMARY
Neglected tropical diseases such as soil-transmitted helminthiasis (STH) and schistosomiasis (SCH) afflict over a billion people in low-resource areas, 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 egg detection and classification, and a user-friendly verification interface for technicians to review results. Designed with consideration of World Health Organization diagnostic needs for NTDs, the platform was tested in laboratory and zero-infrastructure field trials in Ethiopia and Uganda, it processed nearly 1,000 slides, detecting STH and SCH with over 90% precision and recall. Field users noted improvements in portability and ease of use, though scan times remain slower than manual methods. While the AI-DP platform shows potential as a tool for efficient monitoring and evaluation of STH and SCH 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.
