ArticleThe American journal of tropical medicine and hygiene2022
Performance Evaluation of the Schistoscope 5.0 for (Semi-)automated Digital Detection and Quantification of Schistosoma haematobium Eggs in Urine: A Field-based Study in Nigeria.
Article in The American journal of tropical medicine and hygiene, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Accuracy of AI-assisted diagnostic tools for Schistosoma haematobium: A systematic review and meta-analysis.PLoS neglected tropical diseases · 2026Pooled it
- Diagnostic accuracy of rapid and point-of-care tests forFrontiers in parasitology · 2026Pooled it
- AI-Supported Digital Microscopy Diagnostics in Primary Health Care Laboratories: Scoping Review.Journal of medical Internet research · 2026Article
- Multi-contrast machine learning improves schistosomiasis diagnostic performance.PLoS neglected tropical diseases · 2025Article
- Deep learning-based automated detection and multiclass classification of soil-transmitted helminths and Schistosoma mansoni eggs in fecal smear images.Scientific reports · 2025Article
- Evaluation of the AiDx Assist device for automated detection ofFrontiers in parasitology · 2025Article
- Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria.International journal of environmental research and public health · 2024Article
- Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.PLoS neglected tropical diseases · 2024Article
- Review
- Validation of artificial intelligence-based digital microscopy for automated detection of Schistosoma haematobium eggs in urine in Gabon.PLoS neglected tropical diseases · 2024Article
- Reproducibility matters: intra- and inter-sample variation of the point-of-care circulating cathodic antigen test in twoPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- Review of Recent Prevalence of Urogenital Schistosomiasis in Sub-Saharan Africa and Diagnostic Challenges in the Field Setting.Life (Basel, Switzerland) · 2023Review
- Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Developing inclusive digital health diagnostic for schistosomiasis: a need for guidance via target product profiles.Frontiers in parasitology · 2023Article
- Diagnosis of Schistosomiasis without a Microscope: Evaluating Circulating Antigen (CCA, CAA) and DNA Detection Methods on Banked Samples of a Community-Based Survey from DR Congo.Tropical medicine and infectious disease · 2022Article
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Authors and funding
10 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conventional microscopy is the standard procedure for the diagnosis of schistosomiasis, despite its limited sensitivity, reliance on skilled personnel, and the fact that it is error prone. Here, we report the performance of the innovative (semi-)automated Schistoscope 5.0 for optical digital detection and quantification of Schistosoma haematobium eggs in urine, using conventional microscopy as the reference standard. At baseline, 487 participants in a rural setting in Nigeria were assessed, of which 166 (34.1%) tested S. haematobium positive by conventional microscopy. Captured images from the Schistoscope 5.0 were analyzed manually (semiautomation) and by an artificial intelligence (AI) algorithm (full automation). Semi- and fully automated digital microscopy showed comparable sensitivities of 80.1% (95% confidence interval [CI]: 73.2-86.0) and 87.3% (95% CI: 81.3-92.0), but a significant difference in specificity of 95.3% (95% CI: 92.4-97.4) and 48.9% (95% CI: 43.3-55.0), respectively. Overall, estimated egg counts of semi- and fully automated digital microscopy correlated significantly with the egg counts of conventional microscopy (r = 0.90 and r = 0.80, respectively, P < 0.001), although the fully automated procedure generally underestimated the higher egg counts. In 38 egg positive cases, an additional urine sample was examined 10 days after praziquantel treatment, showing a similar cure rate and egg reduction rate when comparing conventional microscopy with semiautomated digital microscopy. In this first extensive field evaluation, we found the semiautomated Schistoscope 5.0 to be a promising tool for the detection and monitoring of S. haematobium infection, although further improvement of the AI algorithm for full automation is required.
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