ArticlePLoS neglected tropical diseases2024
Validation of artificial intelligence-based digital microscopy for automated detection of Schistosoma haematobium eggs in urine in Gabon.
Article in PLoS neglected tropical diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04505046 (Validation of INSPiRED Innovative Smart Diagnostic Devices for the Detection of Plasmodium Falciparum, Schistosoma Haematobium and Necator Americanus at CERMEL, Gabon.), which is not on this map. Cited by 11 papers, 2 of them syntheses that pooled it.
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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.
Validation of INSPiRED Innovative Smart Diagnostic Devices for the Detection of Plasmodium Falciparum, Schistosoma Haematobium and Necator Americanus at CERMEL, Gabon.
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Diagnostic accuracy of plasma cell-free DNA qPCR for Schistosoma haematobium assessed by Bayesian latent class analysis in a cohort of pregnant women from Lambaréné, Gabon.Infectious diseases of poverty · 2026Article
- AI-Supported Digital Microscopy Diagnostics in Primary Health Care Laboratories: Scoping Review.Journal of medical Internet research · 2026Article
- NTDscope: A multi-contrast portable microscope for disease diagnosis.PLOS global public health · 2026Article
- Portable digital microscopy with point-of-care testing for low-cost and efficient prevalence surveys for schistosomiasis control.PLoS neglected tropical diseases · 2025Article
- Multi-contrast machine learning improves schistosomiasis diagnostic performance.PLoS neglected tropical diseases · 2025Article
- Polyparasitic Infections: Associated Factors and Effect on the Haemoglobin Level of Children Living in Lambaréné Remote and Surrounding Rural Areas from Gabon-A Cross-Sectional Study.Tropical medicine and infectious disease · 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
- 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
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
introductionSchistosomiasis is a significant public health concern, especially in Sub-Saharan Africa. Conventional microscopy is the standard diagnostic method in resource-limited settings, but with limitations, such as the need for expert microscopists. An automated digital microscope with artificial intelligence (Schistoscope), offers a potential solution. This field study aimed to validate the diagnostic performance of the Schistoscope for detecting and quantifying Schistosoma haematobium eggs in urine compared to conventional microscopy and to a composite reference standard (CRS) consisting of real-time PCR and the up-converting particle (UCP) lateral flow (LF) test for the detection of schistosome circulating anodic antigen (CAA).
methodsBased on a non-inferiority concept, the Schistoscope was evaluated in two parts: study A, consisting of 339 freshly collected urine samples and study B, consisting of 798 fresh urine samples that were also banked as slides for analysis with the Schistoscope. In both studies, the Schistoscope, conventional microscopy, real-time PCR and UCP-LF CAA were performed and samples with all the diagnostic test results were included in the analysis. All diagnostic procedures were performed in a laboratory located in a rural area of Gabon, endemic for S. haematobium.
resultsIn study A and B, the Schistoscope demonstrated a sensitivity of 83.1% and 96.3% compared to conventional microscopy, and 62.9% and 78.0% compared to the CRS. The sensitivity of conventional microscopy in study A and B compared to the CRS was 61.9% and 75.2%, respectively, comparable to the Schistoscope. The specificity of the Schistoscope in study A (78.8%) was significantly lower than that of conventional microscopy (96.4%) based on the CRS but comparable in study B (90.9% and 98.0%, respectively).
conclusionOverall, the performance of the Schistoscope was non-inferior to conventional microscopy with a comparable sensitivity, although the specificity varied. The Schistoscope shows promising diagnostic accuracy, particularly for samples with moderate to higher infection intensities as well as for banked sample slides, highlighting the potential for retrospective analysis in resource-limited settings.
trial registrationNCT04505046 ClinicalTrials.gov.
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