Agentic education · Learning science

Actionable companion · Assessment & reasoning

Learning with agency: Diagnosis of soil-transmitted helminth infections with digital mobile microscopy and artificial intelligence in a resource-limited 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.

Read original paperSearch paper agentCC BY 4.0

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): Primary school, K–12 · mixed / unspecified, Children · stage unspecified. Verify the participant population and task before transferring the findings.

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

Abstract Background Infections caused by soil-transmitted helminths (STHs) are the most prevalent neglected tropical diseases and result in a major disease burden in low- and middle-income countries, especially in school-aged children. Improved diagnostic methods, especially for light intensity infections, are needed for efficient, control and elimination of STHs as a public health problem, as well as STH management. Image-based artificial intelligence (AI) has shown promise for STH detection in digitized stool samples. However, the diagnostic accuracy of AI-based analysis of entire microscope slides, so called whole-slide images (WSI), has previously not been evaluated on a sample-level in primary healthcare settings in STH endemic countries. Methodology/Principal findings Stool samples (n = 1,335) were collected during 2020 from children attending primary schools in Kwale County, Kenya, prepared according to the Kato-Katz method at a local primary healthcare laboratory and digitized with a portable whole-slide microscopy scanner and uploaded via mobile networks to a cloud environment. The digital samples of adequate quality (n = 1,180) were split into a training (n = 388) and test set (n = 792) and a deep-learning system (DLS) developed for detection of STHs. The DLS findings were compared with expert manual microscopy and additional visual assessment of the digital samples in slides with discordant results between the methods. Manual microscopy detected 15 (1.9%) Ascaris lumbricoides, 172 (21.7%) Tricuris trichiura and 140 (17.7%) hookworm (Ancylostoma duodenale or Necator americanus) infections in the test set. Importantly, more than 90% of all STH positive cases represented light intensity infections. With manual microscopy as the reference standard, the sensitivity of the DLS as the index test for detection of A. lumbricoides, T. trichiura and hookworm was 80%, 92% and 76%, respectively. The corresponding specificity was 98%, 90% and 95%. Notably, in 79 samples (10%) classified as negative by manual microscopy for a specific species, STH eggs were detected by the DLS and confirmed correct by visual inspection of the digital samples. Conclusions/Significance Analysis of digitally scanned stool samples with the DLS provided high diagnostic accuracy for detection of STHs. Importantly, a substantial number of light intensity infections were missed by manual microscopy but detected by the DLS. Thus, analysis of WSIs with image-based AI may provide a future tool for improved detection of STHs in a primary healthcare setting, which in turn could facilitate monitoring and evaluation of control programs. Author summary In this school survey conducted within a primary healthcare setting in rural Kenya, a deep-learning system (DLS) was developed for detection of eggs secreted in human stool from intestinal worms, so called soil-transmitted helminths (STHs). Infections caused by STHs are the most common neglected tropical diseases and a major cause of health impairment in low- and middle-income countries. Novel diagnostic methods are needed to improve disease control. In the current study, stool samples were collected from children attending local primary schools and digitized using a portable slide scanner and uploaded via mobile networks to a cloud repository. Using the digital samples, a DLS was trained and tested for the detection of eggs of the most common STHs (Ascaris lumbricoides, Tricuris trichiura and hookworms) and compared to conventional manual microscopy analysis of the samples. The results showed that the DLS was able to accurately detect STHs in the digital samples. Although the DLS detected some false positive parasites, it was able to detect a significant number of parasites that had been missed with conventional microscopy analysis. The study concludes that digital microscopy, supported by image-based artificial intelligence, can be implemented in a primary health care setting and may provide a future tool for improved STH detection and, thereby, better monitoring and evaluation of control programs. The digital method is especially promising in light intensity infections, with only a few parasite eggs per sample.

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Primary school; K–12 · mixed / unspecified; 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

Ethical approval for the current study (No: TUM ERC EXT/001/2020) was issued by the Chairperson of the Ethical Review Committee (Mombasa, Kenya) of the Technical University of Mombasa accredited by the National Commission for Science, Technology and Innovation Kenya. It was emphasized that participation in the study was voluntary and withdrawal possible at any stage without further obligation. Informed verbal assent for study participation was acquired from participants and their parents or legal guardian, and a written consent form (in Swahili and English) was signed by the parents or legal guardian of the participant, as well as by the headmaster of the primary schools. Following the study, children in all participating schools were offered preventive treatment (albendazole at a dose of 400 mg), according to national guidelines. All participants with detected STHs were also examined by a clinician, besides prescribing the deworming medication. Treatment was offered by a designated clinician from the study hospital (Kinondo Kwetu Hospital). There was a follow-up on treated infected patients that was conducted by the Kwale County NTD Program coordinator who also provided the necessary drugs. The National NTD Program management was included in the treatment for effective follow up. Furthermore, the families of children with positive tests were also offered deworming treatment.

Limitations and uncertainty · source passage

For the object classification, transfer learning [35] was utilized using a pre-trained ResNet50 network. The algorithm was a multiclass classification convolutional neural network which was trained using manually selected image regions measuring 150 x 150 px2 (72 x 72 μm) in the training samples. These areas represented visible A. lumbricoides, T. trichiura, hookworm and artefacts. Training of the classifier was performed in 50 training epochs with a minibatch size of 32, using a stochastic gradient descent solver with a momentum of 0.9 and initial learning rate of 0.003 and dropping the learning rate by half after every 10 epochs. Image augmentations of the training data were utilized to increase the generalizability of the model and to prevent overfitting for both networks in the DLS. For the detection network, augmentations used were variation of scale (±10%), XY reflections, saturation offset (0.1), brightness offset (0.1), hue offset (0.05), and scale factor of contrast (0.1). The object classifier training data was augmented using variation of scale (±5%), rotation (0–360°), XY shear (±5°), XY reflections, XY translations (±10 pixels), saturation offset (0.1), brightness offset (0.15), hue offset (0.05), and scale factor of contrast (0.15).

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.

  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

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. Pilot one teacher-led unit with short guided turns. The tutor asks for an explanation and offers a graduated hint before any worked solution. Use concrete examples and an equivalent task without AI. 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. Use an age-appropriate independent post-task and a delayed parallel task. Score explanation quality and transfer, not the number of agent messages or completion rate alone. 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

Johan Lundin; Antti Suutala; Oscar Holmström; Samuel Henriksson; Severi Valkamo; Harrison Kaingu; Felix Kinyua; Martin Muinde; Mikael Lundin; Vinod Diwan; Andreas Mårtensson; Nina Linder. Diagnosis of soil-transmitted helminth infections with digital mobile microscopy and artificial intelligence in a resource-limited setting. PLOS Neglected Tropical Diseases (2024-04-11). https://doi.org/10.1371/journal.pntd.0012041

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.