ArticleScientific reports2025
AI-supported versus manual microscopy of Kato-Katz smears for diagnosis of soil-transmitted helminth infections in a primary healthcare setting.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Development and validation of the AI-predictive ParaScoutEmerging microbes & infections · 2026Article
- Deep learning applications in parasite microscopy: A scoping review.PLoS neglected tropical diseases · 2026Article
- Diagnostic Sensitivity of the McMaster Technique for Detecting Intestinal Parasites in Human Populations: A Systematic Review and Meta-Analysis.Tropical medicine and infectious disease · 2026Review
- Development and Validation of a Computer Vision-Based Artificial Intelligence System (FenoParasite) for the Microscopic Detection ofTropical medicine and infectious disease · 2026Article
- A Magnetic-Assisted CRISPR-Cas12a Biosensor Incorporating a Y-DNA Probe for Sensitive Detection ofBiosensors · 2026Article
- Comparative evaluation of Midi ParasepParasites & vectors · 2026Article
- SWOT analysis on veterinary telemedicine from pet owner and expert perspectives-a mixed-methods survey and interview study in Germany.Frontiers in veterinary science · 2026Article
- Severe hypereosinophilia secondary to intestinal whipworm infection with negative stool tests: a case report.Frontiers in medicine · 2026Article
- Acute appendicitis caused byIDCases · 2025Article
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14 authors.
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Abstract
Soil-transmitted helminths primarily comprise Ascaris lumbricoides, Trichuris trichiura, and hookworms, infecting more than 600 million people globally, particularly in underserved communities. Manual microscopy of Kato-Katz thick smears is a widely used diagnostic method in monitoring and control programs, but is time-consuming, requires on-site experts and has low sensitivity, especially for light intensity infections. In this study, portable whole-slide scanners and deep learning-based artificial intelligence (AI) were deployed in a primary healthcare setting in Kenya. Stool samples (n = 965) were collected from school children and Kato-Katz thick smears were digitized for AI-based detection. Light-intensity infections accounted for 96.7% of cases. Three diagnostic methods - manual microscopy, autonomous AI and human expert-verified AI - were compared to a composite reference standard, which combined expert-verified helminth eggs in physical and digital smears. Sensitivity for A. lumbricoides, T. trichiura and hookworms was 50.0%, 31.2%, and 77.8% for manual microscopy; 50.0%, 84.4%, and 87.4% for the autonomous AI; and 100%, 93.8%, and 92.2% for expert-verified AI in smears suitable for analysis (n = 704). Specificity exceeded 97% across all methods. The expert-verified AI had higher sensitivity than the other methods while maintaining high specificity for the detection of soil-transmitted helminths in Kato-Katz thick smears, especially in light-intensity infections.
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