ArticlePloS one2024
A comprehensive evaluation of an artificial intelligence based digital pathology to monitor large-scale deworming programs against soil-transmitted helminths: A study protocol.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06055530 (A Comprehensive Evaluation of an Artificial Intelligence Based Digital Pathology to Monitor Large-scale Deworming Programs Against Soil-transmitted Helminths), which is not on this map. Cited by 5 papers.
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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.
A Comprehensive Evaluation of an Artificial Intelligence Based Digital Pathology to Monitor Large-scale Deworming Programs Against Soil-transmitted Helminths
Who cites it
5 citing papers 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
- An artificial intelligence-powered digital pathology platform to support large-scale deworming programs against soil-transmitted helminthiasis and intestinal schistosomiasis in resource-limited settings.PLoS neglected tropical diseases · 2026Article
- Multi-contrast machine learning improves schistosomiasis diagnostic performance.PLoS neglected tropical diseases · 2025Article
- Detecting soil-transmitted helminth and Schistosoma mansoni eggs in Kato-Katz stool smear microscopy images: A comprehensive in- and out-of-distribution evaluation of YOLOv7 variants.PLoS neglected tropical diseases · 2025Article
- Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy.PLoS neglected tropical diseases · 2024Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundManual 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.
methodsHere, 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 registrationThe trial was registered on September 29, 2023 Clinicaltrials.gov (ID: NCT06055530).
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