Agentic education · Learning science

Actionable companion · Learning design & outcomes

Learning with agency: AI-supported versus manual microscopy of Kato-Katz smears for diagnosis of soil-transmitted helminth infections in a primary healthcare setting

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Children · stage unspecified. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

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. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-07309-7. Keywords: Digital diagnostics, Deep learning, Neglected tropical diseases, Primary health care, Point-of-care, Whole slide imaging Subject terms: Infectious diseases, Parasitic infection, Computer science, Diagnosis, Public health

What kind of evidence is this?

Supporting AI learning science · Design requires full-text appraisal

Stage: Children · stage unspecified. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

The diagnostic accuracy of three methods for detection of STHs in Kato-Katz thick smears was compared to a composite reference standard. The methods were: manual microscopy, an autonomous AI-based digital method and an expert-verified AI. To evaluate these three methods, 965 stool samples were collected from school children in Kwale County, Kenya. The study was conducted at the Kinondo Kwetu Hospital (https://www.kinondokwetuhospital.com), a primary health care hospital, owned and supported by a trust fund (Kinondo Kwetu Trust Fund). The study flow is presented in accordance with the Standards for Reporting of Diagnostic Accuracy Studies (STARD)-guidelines in Fig. 324.

Limitations and uncertainty · source passage

The World Health Organization (WHO) currently recommends microscopy of stool samples prepared using the Kato-Katz technique for diagnostic tasks, such as large-scale monitoring of STH infections within mass drug administration programs and epidemiological surveys because of its simplicity, ease-of-use and ability to classify infection intensity7,8. The infection intensity is classified as either light, moderate, or high by quantifying parasite eggs per gram (EPG) in stool, and has clinical relevance as the intensity is correlated to the severity of symptoms5,9. However, limitations of microscopy of Kato-Katz smears include that an expert microscopist is required to be on-site, manual microscopy is time consuming and has low sensitivity especially for light intensity infections. The Kato-Katz technique requires the sample to be analyzed within 30–60 min, as glycerol causes disintegration of hookworm eggs10,11. Therefore, well trained on-site microscopists capable of performing the analysis on demand are required9.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Establish the participants’ ages, developmental needs and educational setting from the full paper before selecting an intervention. Use adult-mediated activity and preserve hands-on and peer learning. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Evaluate an age-appropriate independent task after the interaction. Do not infer a preschool or primary-school effect when the paper does not establish that population. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Joar von Bahr; Antti Suutala; Hakan Kucukel; Harrison Kaingu; Felix Kinyua; Martin Muinde; Kevan Osundwa; Wigina Ronald; Jackson Muinde; Billy Ngasala; Mikael Lundin; Andreas Mårtensson; Nina Linder; Johan Lundin. AI-supported versus manual microscopy of Kato-Katz smears for diagnosis of soil-transmitted helminth infections in a primary healthcare setting. Scientific Reports (2025-06-27). https://doi.org/10.1038/s41598-025-07309-7

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.