ArticlePLoS neglected tropical diseases2026
An artificial intelligence-powered digital pathology platform to support large-scale deworming programs against soil-transmitted helminthiasis and intestinal schistosomiasis in resource-limited settings.
Article in PLoS neglected tropical diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Evaluation of six different tests for Schistosoma haematobium diagnosis in a near-elimination setting: A prospective observational diagnostic accuracy study.PLoS neglected tropical diseases · 2026Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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
findingsThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.